{ "metadata": { "name": "", "signature": "sha256:3627762d7c5571b4b41ee8b2fe7bfc875d560ccaaca35ebfef14b1ded1ddf449" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import json\n", "from instagram.client import InstagramAPI\n", "from urllib import urlopen\n", "from pprint import pprint\n", "\n", "client_id = \"c58899640d8943ccab8d1cacfaced36a\"\n", "base_url = \"https://api.instagram.com/v1\"\n", "endpoints = \"users\"\n", "\n", "def get_uid(user_list):\n", " uid = {}\n", " for user_name in user_list:\n", " url = \"{0}/{1}/search?q={2}&client_id={3}\".format(base_url, endpoints, user_name, client_id)\n", " data = urlopen(url).read()\n", " response = json.loads(data)\n", "\n", " uid[user_name] = response['data'][0]['id']\n", " return uid\n", "\n", "# Collect user id of top fashion posters\n", "# Later we'll use this as golden standard to classify posts as fashion or non-fashion\n", "users = ['violettaroom', 'makeuptoolshop', 'afnirohijab', 'aura_glow', 'addictshopaholic']\n", "pprint (get_uid(users))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "{'addictshopaholic': u'367660971',\n", " 'afnirohijab': u'466287585',\n", " 'aura_glow': u'344848804',\n", " 'makeuptoolshop': u'242236159',\n", " 'violettaroom': u'1194779606'}\n" ] } ], "prompt_number": 112 }, { "cell_type": "code", "collapsed": false, "input": [ "# Fetch next posts batch\n", "def get_next_page(content):\n", " if 'pagination' in content:\n", " if 'next_url' in content['pagination']:\n", " return content['pagination']['next_url']\n", " else:\n", " return ''\n", " else:\n", " return ''" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "corpus = ''\n", "count_caption,count_stores = 0,1\n", "\n", "f = open('insta_corpus_raw.txt', 'w')\n", "for user_id in uid.values():\n", " url = \"{0}/{1}/{2}/media/recent/?client_id={3}\".format(base_url, endpoints, user_id, client_id)\n", " while url != '':\n", " next_data = urlopen(url).read()\n", " response = json.loads(next_data)\n", " for i in range(0, len(response['data'])):\n", " if 'data' in response:\n", " if response['data'][i]['caption'] is not None:\n", " f.write(response['data'][i]['caption']['text'].encode('utf-8'))\n", " count_caption += 1\n", " url = get_next_page(response)\n", " print 'Crawled {0} posts from {1} stores'.format(count_caption, count_stores)\n", " count_stores += 1\n", "\n", "print \"Processing completed\"\n", "f.close()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Crawled 20 posts from 1 stores\n", "Crawled 40 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 60 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 80 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 100 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 120 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 140 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 160 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 180 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 200 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 220 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 240 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 260 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 280 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 299 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 318 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 338 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 358 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 378 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 398 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 418 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 438 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 458 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 478 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 498 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 518 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 538 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 558 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 578 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 598 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 618 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 638 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 658 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 678 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 681 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 701 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 721 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 741 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 761 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 781 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 801 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 821 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 841 posts from 3 stores" ] }, { "output_type": 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}, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1003 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1007 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1025 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1045 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1065 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1085 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1105 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1125 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1145 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1158 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1177 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1197 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1214 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1227 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1247 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1267 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1280 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1280 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1280 posts from 3 stores" ] }, { "output_type": "stream", 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}, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1292 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1295 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1302 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1315 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1330 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1350 posts from 4 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1370 posts from 4 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1390 posts from 4 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1410 posts from 4 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", 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}, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1809 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1829 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1848 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1868 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1888 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1908 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1928 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1947 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1967 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1987 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2007 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2027 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2047 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2067 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2086 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2106 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2126 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2146 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2166 posts from 5 stores" ] }, { "output_type": "stream", 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}, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2960 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2978 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2997 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3015 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3035 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3055 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3075 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3093 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3111 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3131 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3148 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Processing completed\n" ] } ], "prompt_number": 56 }, { "cell_type": "code", "collapsed": false, "input": [ "import re\n", "import nltk\n", "\n", "# Load stopwords\n", "def load_stopwords():\n", " stopwords = []\n", " f = open(\"stopwords.txt\")\n", " for line in f.readlines():\n", " stopwords.append(line.replace('\\n', '').replace('\\r', ''))\n", " f.close()\n", " return stopwords\n", "\n", "# Filter message\n", "def filter_msg(msg):\n", " # remove stopwords\n", " msg = [i for i in msg.split() if i not in stopwords]\n", " return ' '.join(msg)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 44 }, { "cell_type": "code", "collapsed": false, "input": [ "# Clean insta_corpus_raw.txt, remove hashtags, smiley, multiple spaces, links\n", "# Rewrite to insta_corpus_cleaned.txt\n", "stopwords = load_stopwords()\n", "\n", "f_raw = open('insta_corpus_raw.txt', 'r')\n", "f_cleaned = open('insta_corpus_cleaned.txt', 'w')\n", "for line in f_raw.readlines():\n", " if line == '\\n':\n", " continue\n", " cap_text = re.sub(\"(@[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+:\\/\\/\\S+)\", \" \", line).lower() # Removing hashtags\n", " cap_text = ''.join([i for i in cap_text if not i.isdigit()]) # Removing numbers\n", " cap_text = filter_msg(cap_text) # Removing stopwords\n", " cap_text = re.sub(' +',' ',cap_text) # Removing multiple spaces\n", " f_cleaned.write(cap_text)\n", "f_cleaned.close()\n", "f_raw.close()\n", "print \"Processing completed\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Processing completed\n" ] } ], "prompt_number": 72 }, { "cell_type": "code", "collapsed": false, "input": [ "f = open('insta_corpus_cleaned.txt', 'r')\n", "corpus = f.read()\n", "f.close()\n", "\n", "token = nltk.word_tokenize(corpus)\n", "print len(token)\n", "key_words = nltk.FreqDist(token).most_common()[:2000]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "49832\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "def document_features(document):\n", " document_words = set(document)\n", " features = {}\n", " for word in key_words:\n", " features['contain(%s)' % word[0]] = (word[0] in document_words)\n", " return features" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "# getting posts from non fashion posters\n", "users = ['blastacake_sita']\n", "uid = get_uid(users)\n", "\n", "corpus = ''\n", "count_caption,count_stores = 0,1\n", "\n", "f = open('nonfashion_set_raw.txt', 'w')\n", "for user_id in uid.values():\n", " url = \"{0}/{1}/{2}/media/recent/?client_id={3}\".format(base_url, endpoints, user_id, client_id)\n", " while url != '':\n", " next_data = urlopen(url).read()\n", " response = json.loads(next_data)\n", " for i in range(0, len(response['data'])):\n", " if 'data' in response:\n", " if response['data'][i]['caption'] is not None:\n", " f.write(response['data'][i]['caption']['text'].replace('\\n', ' ').replace('\\r', ' ').encode('utf-8') + '\\n')\n", " count_caption += 1\n", " url = get_next_page(response)\n", " print 'Crawled {0} posts from {1} stores'.format(count_caption, count_stores)\n", " count_stores += 1\n", "\n", "print \"Processing completed\"\n", "f.close()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Crawled 20 posts from 1 stores\n", "Crawled 40 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 60 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 80 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 100 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 120 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 140 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 160 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 180 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 200 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 220 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 240 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 260 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 280 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 300 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 320 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 340 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 360 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 380 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 400 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 420 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 440 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 460 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 480 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 500 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 520 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 540 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 560 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 580 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 600 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 620 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 640 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 660 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 680 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 700 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 720 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 740 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 760 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 780 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 800 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 820 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 840 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 860 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 872 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Processing completed\n" ] } ], "prompt_number": 446 }, { "cell_type": "code", "collapsed": false, "input": [ "# Clean nonfashion_set_raw.txt, remove hashtags, smiley, multiple spaces, links\n", "# Rewrite to nonfashion_set_cleaned.txt\n", "stopwords = load_stopwords()\n", "\n", "f_raw = open('nonfashion_set_raw.txt', 'r')\n", "f_cleaned = open('nonfashion_set_cleaned.txt', 'w')\n", "for line in f_raw.readlines():\n", " if line == '\\n':\n", " continue\n", " cap_text = re.sub(\"(@[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+:\\/\\/\\S+)\", \" \", line).lower() # Removing hashtags\n", " cap_text = ''.join([i for i in cap_text if not i.isdigit()]) # Removing numbers\n", " cap_text = filter_msg(cap_text) # Removing stopwords\n", " cap_text = re.sub(' +',' ',cap_text) # Removing multiple spaces\n", " if cap_text == '' or len(cap_text) < 10:\n", " continue\n", " f_cleaned.write(cap_text + '\\n')\n", "f_cleaned.close()\n", "f_raw.close()\n", "print \"Processing completed\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Processing completed\n" ] } ], "prompt_number": 447 }, { "cell_type": "code", "collapsed": false, "input": [ "# getting posts from fashion posters\n", "users = ['vernalvintage','violettaroom', 'afnirohijab', 'addictshopaholic','makeuptoolshop', 'aura_glow']\n", "uid = get_uid(users)\n", "\n", "corpus = ''\n", "count_caption,count_stores = 0,1\n", "\n", "f = open('fashion_set_raw.txt', 'w')\n", "for user_id in uid.values():\n", " url = \"{0}/{1}/{2}/media/recent/?client_id={3}\".format(base_url, endpoints, user_id, client_id)\n", " while url != '':\n", " next_data = urlopen(url).read()\n", " response = json.loads(next_data)\n", " for i in range(0, len(response['data'])):\n", " if 'data' in response:\n", " if response['data'][i]['caption'] is not None:\n", " f.write(response['data'][i]['caption']['text'].replace('\\n', ' ').replace('\\r', ' ').encode('utf-8') + '\\n')\n", " count_caption += 1\n", " url = get_next_page(response)\n", " print 'Crawled {0} posts from {1} stores'.format(count_caption, count_stores)\n", " count_stores += 1\n", "\n", "print \"Processing completed\"\n", "f.close()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Crawled 20 posts from 1 stores\n", "Crawled 40 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 60 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 80 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 100 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 120 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 140 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 160 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 180 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 200 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 220 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 240 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 260 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 280 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 300 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 319 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 337 posts from 1 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 357 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 377 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 397 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 417 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 437 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 457 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 476 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 496 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 515 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 535 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 555 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 575 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 595 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 614 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 634 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 654 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 674 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 694 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 714 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 734 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 753 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 773 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 793 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 813 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 833 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 853 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 873 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 893 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 913 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 933 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 953 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 973 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 993 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1013 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1032 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1052 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1072 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1092 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1112 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1132 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1152 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1172 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1192 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1212 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1232 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1252 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1272 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1292 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1312 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1332 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1352 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1372 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1391 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1409 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1429 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1448 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1468 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1488 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1508 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1528 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1548 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1568 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1588 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1607 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1627 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1645 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1664 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1682 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1702 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1722 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1742 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1760 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1778 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1798 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1816 posts from 2 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1836 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1856 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1876 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1896 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1916 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1936 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1956 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1976 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 1996 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2016 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2036 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2056 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2076 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2096 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2116 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2136 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2156 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2176 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2179 posts from 3 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2199 posts from 4 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2217 posts from 4 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2237 posts from 4 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2257 posts from 4 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2277 posts from 4 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2297 posts from 4 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2317 posts from 4 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2337 posts from 4 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2356 posts from 4 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2376 posts from 4 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2396 posts from 4 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2416 posts from 4 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2436 posts from 4 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2455 posts from 4 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2473 posts from 4 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2493 posts from 4 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2513 posts from 4 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2532 posts from 4 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2542 posts from 4 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2545 posts from 4 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2565 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2585 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2605 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2625 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2645 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2665 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2685 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2705 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2723 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2743 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2763 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2782 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2802 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2820 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2840 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2859 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2879 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2885 posts from 5 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2905 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2925 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2945 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2965 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 2985 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3005 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3025 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3045 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3065 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3085 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3104 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3119 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3139 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3151 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3165 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3177 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3184 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3196 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3207 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3211 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3229 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3249 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3269 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3289 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3309 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3329 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3349 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3362 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3381 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3401 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3418 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3431 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3451 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3471 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3484 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3484 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3484 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3486 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3486 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3486 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3486 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3486 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3488 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3489 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3491 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3491 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3494 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3496 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3499 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3506 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3519 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Crawled 3534 posts from 6 stores" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Processing completed\n" ] } ], "prompt_number": 448 }, { "cell_type": "code", "collapsed": false, "input": [ "# Clean raw_data.txt, remove hashtags, smiley, multiple spaces, links\n", "# Rewrite to data_cleaned.txt\n", "stopwords = load_stopwords()\n", "\n", "f_raw = open('fashion_set_raw.txt', 'r')\n", "f_cleaned = open('fashion_set_cleaned.txt', 'w')\n", "for line in f_raw.readlines():\n", " if line == '\\n':\n", " continue\n", " cap_text = re.sub(\"(@[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+:\\/\\/\\S+)\", \" \", line).lower() # Removing hashtags\n", " cap_text = ''.join([i for i in cap_text if not i.isdigit()]) # Removing numbers\n", " cap_text = filter_msg(cap_text) # Removing stopwords\n", " cap_text = re.sub(' +',' ',cap_text) # Removing multiple spaces\n", " if cap_text == '' or len(cap_text) < 10:\n", " continue\n", " f_cleaned.write(cap_text + '\\n')\n", "f_cleaned.close()\n", "f_raw.close()\n", "print \"Processing completed\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Processing completed\n" ] } ], "prompt_number": 449 }, { "cell_type": "code", "collapsed": false, "input": [ "posts = ''\n", "\n", "# Add fashion set to the training and test set\n", "f = open('fashion_set_cleaned.txt', 'r')\n", "posts = [(nltk.word_tokenize(line), 'fas')\n", " for line in f.readlines()]\n", "f.close()\n", "\n", "# Create training and test set from non-fashion set\n", "f = open('nonfashion_set_cleaned.txt', 'r')\n", "posts += ([(nltk.word_tokenize(line), 'non')\n", " for line in f.readlines()])\n", "f.close()\n", "\n", "# Shuffle the training and test set\n", "import random\n", "random.shuffle(posts)\n", "print posts" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[(['auraglow', 'pusatnya', 'auraglow', 'skincare', 'bersertifikat', 'bpom', 'bebas', 'mercury', 'bebas', 'hidroquinon', 'zat', 'berbahaya', 'lihat', 'buktikan', 'auraglow', 'skincare', 'perawatan', 'kulit', 'cantikmu', 'auraglow', 'fix', 'novie', 'ezzty', 'fast', 'hp', 'bf', 'skincare', 'pusatauraglow', 'cosmetics', 'pemutihwajah', 'pemutihbadan', 'perawatanwajah', 'creamherbal', 'perawatanbadan', 'perawatanmuka', 'obatjerawat', 'auragloworiginal', 'perawatanmurah', 'auraglowmurah', 'auraglowskincare', 'creammurah', 'kosmetik', 'creamwajah', 'creampemutih', 'kosmetikmurah', 'ownerauraglow', 'perawatanmuka', 'jualauraglow', 'testiauraglow', 'jualkosmetik', 'creamaman', 'creambpom', 'cantikalami', 'suplierauraglow', 'auraglow', 'auraglow', 'skincare', 'always', 'best'], 'fas'), (['testimonial', 'ysl', 'flap', 'bag', 'mini', 'thankyu', 'loyal', 'customers', 'happy', 'shopping'], 'fas'), (['hand', 'flower', 'xm', 'twistcone', 'recommended', 'filcos', 'ae', 'supplierbaju', 'supplierbajumurah', 'bangkokcollection', 'bangkokstuff', 'aksesoris', 'pobkk', 'cardigan', 'bajucewek', 'jualkalung'], 'fas'), (['dream', 'catcher', 'theme', 'birthdaycupcakes', 'fondantcupcakes', 'fondantfigurine', 'dreamcatcher', 'pastel', 'cupcakeholic', 'cupcakestagram', 'onlinecakejakarta', 'jualancake', 'cakedecorating', 'cakebintaro', 'blastacake'], 'non'), (['bismillah', 'afniro', 'squariyya', 'set', 'soft', 'green', 'squariyya', 'dress', 'squariyya', 'squariyya', 'dress', 'dress', 'cerruty', 'dikombinasikan', 'jersey', 'nursing', 'friendly', 'kancing', 'm', 'sekitarcm', 'pb', 'pb', 'squariyya', 'square', 'terpisah', 'hijab', 'pasminanya', 'kancing', 'menyambungkan', 'matterial', 'cerruty', 'sekitarcm', 'kontak', 'afnirocs', 'format', 'hp', 'alamat', 'orderan', 'hijabsyari', 'hijabsyarijogja', 'hijabjogja', 'hijabstyle', 'hijabindo', 'hijabindonesia', 'jogjakarta', 'jalanman', 'jualhijabmurah', 'jualhijabsyari', 'jualkhimarjogja', 'jualbergojogja', 'bergosyari', 'bergojogja', 'khimarjogja', 'khimarsyari', 'khimarinstan', 'capehijab', 'safiyyacape', 'safiyyacapebyafhj', 'safiyyadress', 'safiyyaset'], 'fas'), (['testimonial', 'biglayer', 'curly', 'cuci', 'catok', 'black', 'dark', 'brown', 'light', 'brown', 'catok', 'farenchan', 'hair', 'hairclip', 'hairclipmurah', 'jualhairclip', 'jualanhairclip', 'jualanhairclipmurah', 'hairclipkorea', 'hairclipjepang', 'infodropship', 'jualanrambut', 'supplierhairclip', 'carihairclip', 'agenhairclip', 'haircliptermurah', 'hairclipbiglayer', 'biglayer', 'olshophairclip', 'hairclipwave', 'carireseller', 'haircliplurus', 'jualcatokan'], 'fas'), (['youuuuu', 'testimuts', 'brush', 'set', 'brush', 'cover', 'www', 'makeuptoolshop', 'com'], 'fas'), (['testimonial', 'biglayer', 'curly', 'm', 'cuci', 'catok', 'black', 'dark', 'brown', 'light', 'brown', 'catok', 'farenchan', 'hair', 'hairclip', 'hairclipmurah', 'jualhairclip', 'jualanhairclip', 'jualanhairclipmurah', 'hairclipkorea', 'hairclipjepang', 'infodropship', 'jualanrambut', 'supplierhairclip', 'carihairclip', 'agenhairclip', 'haircliptermurah', 'hairclipbiglayer', 'biglayer', 'olshophairclip', 'hairclipwave', 'carireseller', 'haircliplurus'], 'fas'), (['auraglow', 'alhamdulilah', 'orderaaan', 'numpukk', 'stiap', 'kirim', 'customers', 'auraglow', 'pengiriman', 'stiap', 'jam', 'siang', 'kcuali', 'libur', 'pusatnya', 'auraglow', 'terbesarr', 'fix', 'novie', 'ezzty', 'fast', 'hp', 'whatsapp', 'bf', 'auraglow', 'always', 'best'], 'fas'), (['upload', 'another', 'account', 'please', 'check', 'afniro', 'afniro', 'afniro', 'available', 'product'], 'fas'), (['weddingcupcake', 'blastacake', 'hellokitty'], 'non'), (['cute', 'hellokitty', 'cake', 'cute', 'baby', 'kalila', 'hellokittycake', 'hellokitty', 'pink', 'pinky', 'birthdaycake', 'instacute', 'jualancake', 'jualkue', 'jualankue', 'kueulangtahun', 'kuejabodetabek', 'cakebintaro', 'cakeart', 'blastacake'], 'non'), (['reshare', 'auraglow', 'terbukti', 'aura', 'glow', 'aman', 'ketergantungan', 'hihiii', 'beda', 'cream', 'berenti', 'wajah', 'kusam', 'jerawat', 'bermunculan', 'tunggu', 'sgra', 'chatting', 'ato', 'detail', 'darling', 'aura', 'glow', 'paket', 'paket', 'normal', 'cream', 'cream', 'sabun', 'collagen', 'paket', 'jerawat', 'cream', 'cream', 'sabun', 'anti', 'acne', 'obat', 'jerawat'], 'fas'), (['drool', 'sponge', 'egg', 'model', 'telur', 'blender', 'model', 'lekuk', 'buah', 'kualitas', 'model'], 'fas'), (['ikutan', 'yuk', 'mutsquiz'], 'fas'), (['eyebrow', 'card', 'set'], 'fas'), (['leopard', 'crop', 'beludru', 'recommended', 'filcos', 'ae', 'supplierbaju', 'supplierbajumurah', 'bangkokcollection', 'bangkokstuff', 'aksesoris', 'pobkk', 'cardigan', 'bajucewek', 'jualkalung'], 'fas'), (['biglayer', 'wavy', 'd', 'cuci', 'catok', 'black', 'dark', 'brown', 'light', 'brown', 'catok', 'farenchan', 'hair', 'hairclip', 'hairclipmurah', 'jualhairclip', 'jualanhairclip', 'jualanhairclipmurah', 'hairclipkorea', 'hairclipjepang', 'infodropship', 'jualanrambut', 'supplierhairclip', 'carihairclip', 'agenhairclip', 'haircliptermurah', 'hairclipbiglayer', 'biglayer', 'olshophairclip', 'hairclipwave', 'carireseller', 'haircliplurus'], 'fas'), (['testimonial', 'biglayer', 'curly', 'cuci', 'catok', 'black', 'dark', 'brown', 'light', 'brown', 'catok', 'farenchan', 'hair', 'hairclip', 'hairclipmurah', 'jualhairclip', 'jualanhairclip', 'jualanhairclipmurah', 'hairclipkorea', 'hairclipjepang', 'infodropship', 'jualanrambut', 'supplierhairclip', 'carihairclip', 'agenhairclip', 'haircliptermurah', 'hairclipbiglayer', 'biglayer', 'olshophairclip', 'hairclipwave', 'carireseller', 'haircliplurus'], 'fas'), (['h', 'blending', 'eye', 'shadow', 'berbentuk', 'cream', 'blending', 'eye', 'shadow', 'berbentuk', 'powder', 'makeuptools', 'makeupbrush', 'jualbrush', 'jualanbrush', 'jualanbulumata', 'jualbulumata', 'onlineshopmedan', 'onlineshopsurabaya', 'onlineshopbangka', 'onlineshoppalembang', 'makeuplover', 'makeupaddict', 'beautyblogger', 'makeupoftheday', 'makeupartistjakarta', 'makeupartistbandung', 'makeupartistpalembang', 'makeupartistmedan', 'makeupartistsurabaya', 'bulumata', 'bulumatapalsu', 'jualbulumatapalsu', 'makeuptoolshop'], 'fas'), (['testimuts', 'brush', 'guard', 'menyenangkan', 'hati', 'gw', 'face', 'brush', 'keceh', 'plastik', 'pembungkus', 'brush', 'guard', 'gw', 'brush', 'travel', 'facebrush', 'brushaddict', 'makeupjungkie', 'clozetteid'], 'fas'), (['black', 'makeup', 'traincase'], 'fas'), (['auraglow', 'skincare', 'pusatauraglow', 'cosmetics', 'pemutihwajah', 'pemutihbadan', 'perawatanwajah', 'creamherbal', 'perawatanbadan', 'perawatanmuka', 'obatjerawat', 'auragloworiginal', 'perawatanmurah', 'auraglowmurah', 'auraglowskincare', 'creammurah', 'kosmetik', 'creamwajah', 'creampemutih', 'kosmetikmurah', 'ownerauraglow', 'perawatanmuka', 'jualauraglow', 'testiauraglow', 'jualkosmetik', 'creamaman', 'creambpom', 'cantikalami', 'suplierauraglow', 'auraglow'], 'fas'), (['brush', 'tree', 'black', 'note', 'brush', 'muat', 'brush'], 'fas'), (['black', 'lace', 'pasang'], 'fas'), (['stone', 'twistcone', 'recommended', 'filcos', 'ae', 'supplierbaju', 'supplierbajumurah', 'bangkokcollection', 'bangkokstuff', 'aksesoris', 'pobkk', 'cardigan', 'bajucewek', 'jualkalung'], 'fas'), (['kayla', 'square', 'berry', 'katun', 'recommended', 'filcos', 'ae', 'supplierbaju', 'supplierbajumurah', 'bangkokcollection', 'bangkokstuff', 'aksesoris', 'pobkk', 'cardigan', 'bajucewek', 'jualkalung'], 'fas'), (['bismillah', 'pre', 'pashimar', 'rainbow', 'lapis', 'deskripsi', 'pasmina', 'lingkaran', 'lapis', 'berbahan', 'cerruty', 'jahit', 'neci', 'full', 'model', 'double', 'hycon', 'm', 'brown', 'green', 'peach', 'pink', 'purple', 'm', 'silakan', 'chat', 'format', 'urut', 'kebawah', 'hp', 'alamat', 'orderan', 'kirim', 'afnirocs', 'shipping', 'katalogponov'], 'fas'), (['reshare', 'auraglow', 'aura', 'glow', 'paket', 'jerawat', 'berfungsi', 'anti', 'bakteri', 'mengurangi', 'jerawat', 'menjegah', 'jerawat', 'kuncinya', 'tetep', 'telaten', 'sabar', 'syg', 'cantik', 'instant', 'kulitnya', 'sehat', 'cantik', 'mengikuti', 'aura', 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'testiauraglow', 'jualkosmetik', 'creamaman', 'creambpom', 'cantikalami', 'suplierauraglow', 'auraglow'], 'fas'), (['birthdaycupcake', 'fondantcupcake', 'apple', 'holland', 'blastacake'], 'non'), (['graduationcupcake', 'graduation', 'fondantcupcake', 'blackandwhite', 'blastacake'], 'non'), (['flamingo', 'set', 'testimuts', 'yeayy', 'make', 'up', 'brushes', 'was', 'coming', 'you', 'so', 'walopun', 'teller', 'dormant', 'sempet', 'was', 'kena', 'tipu', 'tp', 'tyt', 'cepet', 'sampe', 'd', 'makeupyangsuper', 'makeupjunkie', 'makeupjunkies', 'makeupbrush', 'mufybrush'], 'fas'), (['keenan', 'superhero', 'birthday', 'cake', 'superhero', 'superherocake', 'batman', 'superman', 'greenlantern', 'flash', 'spiderman', 'avengers', 'sipermancake', 'batmancake', 'supermancake', 'cake', 'cakeoftheday', 'cakesdaily', 'cakestagram', 'cakebintaro', 'blastacake'], 'non'), (['cake', 'pops', 'mba', 'cakepop', 'cakepops', 'mickeycakepops', 'minniecakepops', 'elmo', 'elmocakepops', 'angrybirds', 'angrybirdscakepops', 'yummy', 'delicious', 'eat', 'goodiebag', 'cakebintaro', 'blastacake'], 'non'), (['youu', 'testimuts', 'lisasoraya', 'alat', 'tempurku', 'huehheheheh', 'lets', 'go', 'bag', 'mixing', 'pallete', 'makeupartist', 'makeupartistbekasi', 'makeuppesta', 'bundamarshamakeupartist'], 'fas'), (['auraglow', 'skincare', 'pusatauraglow', 'cosmetics', 'pemutihwajah', 'pemutihbadan', 'perawatanwajah', 'creamherbal', 'perawatanbadan', 'perawatanmuka', 'obatjerawat', 'auragloworiginal', 'perawatanmurah', 'auraglowmurah', 'auraglowskincare', 'creammurah', 'kosmetik', 'creamwajah', 'creampemutih', 'kosmetikmurah', 'ownerauraglow', 'perawatanmuka', 'jualauraglow', 'testiauraglow', 'jualkosmetik', 'creamaman', 'creambpom', 'cantikalami', 'suplierauraglow', 'auraglow'], 'fas'), (['hapy', 'bday', 'pacarnya', 'larasati', 'goodluck', 'always', 'love', 'loveletter', 'beach', 'cupcakes', 'happy', 'pacar', 'boyfriend', 'ulangtahun', 'recommended', 'yummy', 'instabeautiful', 'cakebintaro', 'blastacake'], 'non'), (['you', 'yaaa', 'testimuts', 'droollashes', 'makeupartistindonesia'], 'fas'), (['birthdaycupcakes', 'fondantcupcakes', 'manchesterunited', 'mufc', 'reddevil', 'cupcakestagram', 'cupcakeholic', 'cakedecor', 'cakeholic', 'baking', 'football', 'jualancake', 'blastacake'], 'non'), (['platinum', 'set'], 'fas'), (['testimuts', 'shadowshields', 'thx', 'shadowshield', 'mua', 'muajakarta', 'makeupartist'], 'fas'), (['thanks', 'ndi', 'au', 'natural', 'eye', 'makeup', 'with', 'paperlashes', 'makeuptoolshop', 'com', 'testimuts'], 'fas'), (['kitty', 'cupcake', 'hbd', 'thaniaa', 'blastacake'], 'non'), (['simple', 'but', 'cute', 'birthday', 'gift', 'birthdaygift', 'cake', 'cakeenak', 'seserahan', 'hantaran', 'ulangtahun', 'kadoultah', 'kadounik', 'cakedecoration', 'baking', 'homemade', 'blastacake'], 'non'), (['you', 'with', 'just', 'love', 'pink', 'love', 'this', 'travel', 'kabuki', 'brush', 'so', 'happy', 'got', 'this', 'smoothly', 'brush', 'd', 'kabukibrush', 'brush', 'pink'], 'fas'), (['auraglow', 'skincare', 'pusatauraglow', 'cosmetics', 'pemutihwajah', 'pemutihbadan', 'perawatanwajah', 'creamherbal', 'perawatanbadan', 'perawatanmuka', 'obatjerawat', 'auragloworiginal', 'perawatanmurah', 'auraglowmurah', 'auraglowskincare', 'creammurah', 'kosmetik', 'creamwajah', 'creampemutih', 'kosmetikmurah', 'ownerauraglow', 'perawatanmuka', 'jualauraglow', 'testiauraglow', 'jualkosmetik', 'creamaman', 'creambpom', 'cantikalami', 'suplierauraglow', 'auraglow'], 'fas'), (['opium', 'colonial', 'table', 'id', 'vnan', 'd'], 'fas'), (['never', 'coast', 'top', 'spandex', 'recommended', 'filcos', 'ae', 'supplierbaju', 'supplierbajumurah', 'bangkokcollection', 'bangkokstuff', 'aksesoris', 'pobkk', 'cardigan', 'bajucewek', 'jualkalung'], 'fas'), (['morniiiiingg', 'cantik', 'jalani', 'wajah', 'cerah', 'sehat', 'putih', 'merona', 'glowing', 'auraglow', 'aman', 'mercury', 'hidroquinon', 'zat', 'berbahaya', 'ask', 'totalan', 'fix', 'smart', 'buyer', 'sista', 'cantik'], 'fas'), (['thankkkssssss', 'so', 'muchhhh', 'testimuts', 'putting', 'green', 'eyeshadow', 'featuring', 'eyeshadow', 'shield', 'courtesy', 'beauty', 'makeup', 'makeupartist', 'makeupartistry', 'workshop', 'seminar', 'photography', 'top', 'model', 'famous', 'people'], 'fas'), (['simple', 'yet', 'elegant', 'cake', 'cake', 'beautiful', 'purplecake', 'purple', 'ribbon', 'ribboncake', 'cute', 'girl', 'beautiful', 'pearl', 'jaksel', 'blastacake'], 'non'), (['ah', 'miss', 'harry', 'potter', 'harrypotter', 'harrypottercake', 'hogwarts', 'gryffindor', 'slytherin', 'hufflepuff', 'ravenclaw', 'buttercream', 'yummy', 'delicious', 'halfblood', 'cakebintaro', 'blastacake'], 'non'), (['d', 'cupcakes', 'hellokitty', 'optimusprime', 'transformer', 'camera', 'laptop', 'customcupcakes', 'cakebintaro', 'blastacake'], 'non'), (['fuschia', 'book', 'note', 'brush', 'booknya', 'testimuts', 'teach', 'tomorrow'], 'fas'), (['drool', 'sponge', 'each'], 'fas'), (['biglayer', 'wave', 'blow', 'cuci', 'catok', 'black', 'dark', 'brown', 'light', 'brown', 'catok', 'farenchan', 'hair', 'hairclip', 'hairclipmurah', 'jualhairclip', 'jualanhairclip', 'jualanhairclipmurah', 'hairclipkorea', 'hairclipjepang', 'infodropship', 'jualanrambut', 'supplierhairclip', 'carihairclip', 'agenhairclip', 'haircliptermurah', 'hairclipbiglayer', 'biglayer', 'olshophairclip', 'hairclipwave', 'carireseller', 'haircliplurus', 'jualcatokan'], 'fas'), (['biglayer', 'curly', 'm', 'cuci', 'catok', 'black', 'dark', 'brown', 'light', 'brown', 'catok', 'farenchan', 'hair', 'hairclip', 'hairclipmurah', 'jualhairclip', 'jualanhairclip', 'jualanhairclipmurah', 'hairclipkorea', 'hairclipjepang', 'infodropship', 'jualanrambut', 'supplierhairclip', 'carihairclip', 'agenhairclip', 'haircliptermurah', 'hairclipbiglayer', 'biglayer', 'olshophairclip', 'hairclipwave', 'carireseller', 'haircliplurus', 'jualcatokan'], 'fas'), (['happy', 'bday', 'yaya', 'cantik', 'cupcakes', 'special', 'tercinta', 'sukses', 'bahagia', 'raisa', 'singer', 'sing', 'birthdaycupcakes', 'cupcakesart', 'cat', 'catlovers', 'kucing', 'penyanyi', 'ulangtahun', 'tosca', 'happybirthday', 'cakebintaro', 'blastacake'], 'non'), (['biglayer', 'wave', 'blow', 'cuci', 'catok', 'black', 'dark', 'brown', 'light', 'brown', 'catok', 'farenchan', 'hair', 'hairclip', 'hairclipmurah', 'jualhairclip', 'jualanhairclip', 'jualanhairclipmurah', 'hairclipkorea', 'hairclipjepang', 'infodropship', 'jualanrambut', 'supplierhairclip', 'carihairclip', 'agenhairclip', 'haircliptermurah', 'hairclipbiglayer', 'biglayer', 'olshophairclip', 'hairclipwave', 'carireseller', 'haircliplurus'], 'fas'), (['handmade', 'lipstick', 'case', 'pc'], 'fas'), (['brush', 'egg', 'mint'], 'fas'), (['neil', 'menguasai', 'kasur', 'hohoho'], 'non'), (['testimonial', 'biglayer', 'curly', 'cuci', 'catok', 'black', 'dark', 'brown', 'light', 'brown', 'catok', 'farenchan', 'hair', 'hairclip', 'hairclipmurah', 'jualhairclip', 'jualanhairclip', 'jualanhairclipmurah', 'hairclipkorea', 'hairclipjepang', 'catokanmurah', 'infodropship', 'jualanrambut', 'supplierhairclip', 'carihairclip', 'agenhairclip', 'haircliptermurah', 'hairclipbiglayer', 'biglayer', 'olshophairclip', 'hairclipwave', 'carireseller', 'haircliplurus'], 'fas'), (['chanel', 'lebboy', 'hot', 'pink', 'real', 'pic', 'check', 'shoppingku'], 'fas'), (['merry', 'bust', 'id', 'vnan'], 'fas'), (['lovely', 'customer', 'wearing', 'pashimar', 'polos', 'm', 'semoga', 'lancar', 'umrohnya', 'amien'], 'fas'), (['pp', 'combination', 'top', 'twistcone', 'recommended', 'filcos', 'ae', 'supplierbaju', 'supplierbajumurah', 'bangkokcollection', 'bangkokstuff', 'aksesoris', 'pobkk', 'cardigan', 'bajucewek', 'jualkalung'], 'fas'), (['pink', 'flat', 'top', 'aplikasi', 'foundation'], 'fas'), (['pop', 'bust', 'id', 'vnan'], 'fas'), (['cq', 'one', 'angled', 'face', 'brush', 'brand', 'cq', 'material', 'synthetic', 'foundation', 'cair', 'mineral', 'powder', 'berhubung', 'modelnya', 'angled', 'miring', 'shading', 'wajah', 'blush', 'brush'], 'fas'), (['drool', 'egg', 'sponge', 'drool', 'blender', 'it', 'sponge', 'pc', 'aplikasi', 'foundation', 'cair', 'cream', 'cream', 'blush', 'shading', 'stick', 'bubuk', 'mineral', 'foundation', 'sponge', 'kering', 'lembab', 'melembabkannya', 'membasahinya', 'kran', 'peras', 'kering', 'sponge', 'sponge', 'lembab', 'sponge', 'membesar', 'kering', 'sponge', 'empuk'], 'fas'), (['lem', 'berfungsi', 'sbg', 'lem', 'bulumata', 'double', 'eyelid', 'each'], 'fas'), (['check', 'me', 'cek', 'hashtag', 'rulesafhj', 'aturan', 'wajib', 'dear', 'fix', 'baca', 'cek', 'hashtag', 'pricelistafhj', 'cek', 'hashtag', 'productinfoafhj', 'cek', 'hashtag', 'afhjavail', 'available'], 'fas'), (['voila', 'tosca', 'blue', 'green', 'fondant', 'fondantcake', 'cake', 'customcakes', 'delicious', 'decoration', 'cakedecorating', 'blastacake'], 'non'), (['yummy', 'oreo', 'macaron', 'this', 'rainy', 'thanks', 'mba', 'testiblastacake', 'testimonial', 'happycustomer', 'happy', 'macaron', 'macarons', 'yummy', 'dessert', 'dessertporn', 'oreo', 'cakebintaro', 'blastacake'], 'non'), (['stainless', 'mixing', 'palette', 'with', 'spatula', 'welcome'], 'fas'), (['kayla', 'stripe', 'twistcone', 'recommended', 'filcos', 'ae', 'supplierbaju', 'supplierbajumurah', 'bangkokcollection', 'bangkokstuff', 'aksesoris', 'pobkk', 'cardigan', 'bajucewek', 'jualkalung'], 'fas'), (['brush', 'iron', 'tersedia', 'mm', 'mm', 'pc', 'brush', 'iron', 'blow', 'natural', 'lurus', 'curly', 'curly', 'loose', 'pinter', 'aman', 'nggak', 'kesundut', 'panas', 'dalamnya', 'sisirnya', 'ngga', 'panas', 'dipegang', 'dipakai', 'tips', 'cepat', 'panas', 'dinyalakan', 'set', 'suhu', 'max', 'c', 'dipakai', 'ngga', 'pake', 'nunggu', 'bersuara', 'ngeblow', 'subuh', 'ngga', 'takut', 'bangunin', 'suami', 'baby', 'pacar', 'tetangga'], 'fas'), (['brush', 'guard', 'pack', 'isi'], 'fas'), (['birthdaycupcakes', 'fondant', 'cupcakes', 'jeep', 'bycycle', 'sepeda', 'camera', 'foodart', 'cupcakeholic', 'cutefigurine', 'cupcakestagram', 'onlinecakejakarta', 'cakebintaro', 'jualancake', 'cakedecorating', 'baking', 'cakelovers', 'blastacake'], 'non'), (['thankkkkkk', 'youuuuu', 'tertimuts', 'never', 'forget', 'brush', 'cleaning', 'make', 'cleaning', 'goes', 'easier', 'using', 'brush', 'egg', 'brush', 'cleanser', 'ps', 'use', 'bigger', 'part', 'brush', 'egg', 'cleaning', 'bigger', 'brushes', 'small', 'part', 'smaller', 'brushes', 'xoxo'], 'fas'), (['necklace', 'peplum', 'thick', 'emboss', 'recommended', 'filcos', 'ae', 'supplierbaju', 'supplierbajumurah', 'bangkokcollection', 'bangkokstuff', 'aksesoris', 'pobkk', 'cardigan', 'bajucewek', 'jualkalung'], 'fas'), (['rainbow', 'cupcake', 'with', 'peanut', 'buttercream', 'blastacake'], 'non'), (['beach', 'theme', 'cupcakes', 'thanks', 'fira', 'cantik', 'ordernya', 'beach', 'beachcupcakes', 'instacute', 'birthdaycupcakes', 'kueulangtahun', 'kueultah', 'doglovers', 'love', 'handmade', 'foodart', 'blue', 'yummy', 'fondant', 'cakebintaro', 'blastacake'], 'non')]\n" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "print len(posts)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "4092\n" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "labeled_posts = ''\n", "\n", "# Add fashion set to the training and test set\n", "f = open('fashion_set_cleaned.txt', 'r')\n", "labeled_posts = [(word, 'fas') for word in set(nltk.word_tokenize(f.read()))]\n", "print len(labeled_posts)\n", "f.close()\n", "\n", "# Create training and test set from non-fashion set\n", "f = open('nonfashion_set_cleaned.txt', 'r')\n", "labeled_posts += [(word, 'non') for word in set(nltk.word_tokenize(f.read()))]\n", "print len(labeled_posts)\n", "f.close()\n", "\n", "# Shuffle the training and test set\n", "import random\n", "random.shuffle(labeled_posts)\n", "print labeled_posts" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "7073\n", "10171" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[('white', 'non'), ('ngebuktiin', 'non'), ('yipee', 'fas'), ('house', 'non'), ('under', 'non'), ('percayakan', 'fas'), ('stitchcupcake', 'non'), ('had', 'fas'), ('cute', 'fas'), ('maceyebrow', 'fas'), ('let', 'fas'), ('self', 'fas'), ('newtools', 'fas'), ('taj', 'fas'), ('nesmy', 'fas'), ('memperdalam', 'fas'), ('elegan', 'fas'), ('keabisan', 'fas'), ('voucher', 'fas'), ('tlp', 'fas'), ('ayooo', 'fas'), ('urut', 'fas'), ('jualanmascara', 'fas'), ('ayyeeeeyy', 'fas'), ('monday', 'non'), ('ever', 'fas'), ('ohsospecialpalette', 'fas'), ('series', 'fas'), ('yaarobbal', 'fas'), ('ayya', 'non'), ('phosphate', 'fas'), ('mahogany', 'fas'), ('shadowshieldsindonesia', 'fas'), ('dpn', 'fas'), ('been', 'fas'), ('recommendedseller', 'fas'), ('jualbracelet', 'fas'), ('neci', 'fas'), ('officially', 'fas'), ('balapan', 'non'), ('wasyukurilkah', 'fas'), ('hihi', 'non'), ('brgnya', 'fas'), ('berkomedo', 'fas'), ('diterima', 'non'), ('tanggungan', 'fas'), ('sakuzip', 'fas'), ('perlahan', 'fas'), ('over', 'fas'), ('tidur', 'fas'), ('nothing', 'non'), ('okay', 'non'), ('brushbook', 'fas'), ('spot', 'fas'), ('have', 'fas'), ('standard', 'non'), ('recommended', 'non'), ('edibleimage', 'non'), ('kepadaku', 'fas'), ('shower', 'fas'), ('don', 'non'), ('babe', 'fas'), ('badger', 'fas'), ('kopi', 'non'), ('javier', 'fas'), ('sikat', 'fas'), ('halal', 'fas'), ('sarungin', 'fas'), ('colagen', 'fas'), ('next', 'fas'), ('ground', 'fas'), ('higher', 'fas'), ('nafiramakeup', 'fas'), ('surau', 'fas'), ('niatnya', 'non'), ('diamond', 'fas'), ('hashtag', 'non'), ('endessssss', 'fas'), ('cuci', 'fas'), ('fuschia', 'fas'), ('wears', 'fas'), ('foodies', 'non'), ('pemakaiannya', 'fas'), ('outer', 'fas'), ('bikinnya', 'non'), ('pandanganmu', 'fas'), ('tali', 'fas'), ('anget', 'fas'), ('soft', 'fas'), ('dihitung', 'fas'), ('touch', 'fas'), ('orderr', 'fas'), ('porsche', 'non'), ('batre', 'fas'), ('primed', 'fas'), ('kemuliaan', 'fas'), ('keriput', 'fas'), ('sujono', 'fas'), ('menyesal', 'fas'), ('temanmu', 'non'), ('blooming', 'fas'), ('memulihkan', 'fas'), ('nomorepruneyhands', 'fas'), ('hand', 'fas'), ('prcaya', 'fas'), ('bulumataaaaaaaaaaa', 'fas'), ('rapunzel', 'non'), ('bold', 'fas'), ('muts', 'fas'), ('naaudzubillahimindzalik', 'fas'), ('seserahan', 'fas'), ('nack', 'fas'), ('gawithbnc', 'non'), ('mempromosikan', 'non'), ('instadaily', 'non'), ('minutesmiraclegel', 'fas'), ('yuks', 'non'), ('complete', 'non'), ('stbirthday', 'non'), ('bbmbroadcast', 'fas'), ('penunjang', 'fas'), ('mas', 'fas'), ('dibentuknya', 'fas'), ('tiki', 'non'), ('yumm', 'non'), ('tolong', 'fas'), ('racing', 'non'), ('avon', 'fas'), ('birthdaay', 'non'), ('spidermancupcake', 'non'), ('nahyaa', 'fas'), ('berbagi', 'fas'), ('princesscake', 'non'), ('hadits', 'fas'), ('tweezers', 'fas'), ('swarovski', 'fas'), ('moodbooster', 'non'), ('ahahaha', 'fas'), ('usa', 'fas'), ('secure', 'fas'), ('fakeeyelash', 'fas'), ('melindahairdo', 'fas'), ('barely', 'fas'), ('stabil', 'non'), ('than', 'fas'), ('weddingmakeup', 'fas'), ('dres', 'fas'), ('bestselling', 'fas'), ('wing', 'fas'), ('dp', 'fas'), ('berry', 'fas'), ('tadaaaa', 'fas'), ('weddingcupcake', 'non'), ('isolasi', 'fas'), ('design', 'fas'), ('makeupcollection', 'fas'), ('even', 'non'), ('cakes', 'non'), ('alias', 'non'), ('greeneyes', 'fas'), ('syam', 'fas'), ('creative', 'fas'), ('bridehalfdone', 'fas'), ('hapy', 'non'), ('bgaimana', 'fas'), ('leexx', 'fas'), ('minion', 'non'), ('antem', 'fas'), ('ways', 'non'), ('gelombang', 'fas'), ('women', 'fas'), ('testimonial', 'non'), ('biaya', 'fas'), ('mixig', 'fas'), ('makeuppontianak', 'fas'), ('kuning', 'fas'), ('thomascupcake', 'non'), ('merk', 'fas'), ('warnain', 'fas'), ('ols', 'fas'), ('eggsponge', 'fas'), ('ovennya', 'non'), ('bner', 'fas'), ('lemburr', 'fas'), ('lengkap', 'fas'), ('babawa', 'fas'), ('paketan', 'fas'), ('productinfoafhj', 'fas'), ('regret', 'fas'), ('bout', 'non'), ('dak', 'fas'), ('viva', 'fas'), ('hkcake', 'non'), ('ruby', 'fas'), ('palettenya', 'fas'), ('butercream', 'non'), ('sweettooth', 'non'), ('keren', 'non'), ('blush', 'fas'), ('juventusclubindonesia', 'non'), ('doa', 'fas'), ('darurat', 'fas'), ('weddingcupcakes', 'non'), ('susa', 'non'), ('mncoba', 'fas'), ('jumlahhh', 'fas'), ('sajahhhh', 'fas'), ('ngeriii', 'fas'), ('contohnya', 'fas'), ('makeupartiststuff', 'fas'), ('anna', 'fas'), ('koran', 'non'), ('diatur', 'fas'), ('tagsforlike', 'non'), ('congrats', 'non'), ('daftar', 'fas'), ('meja', 'fas'), ('kasih', 'fas'), ('sun', 'fas'), ('brushstand', 'fas'), ('memilih', 'fas'), ('kitulecmakeupandbeauty', 'fas'), ('mukena', 'fas'), ('huhu', 'fas'), ('won', 'non'), ('yuuuuhuuu', 'fas'), ('lace', 'fas'), ('hihihih', 'fas'), ('disiapkan', 'fas'), ('ideal', 'fas'), ('net', 'fas'), ('likes', 'fas'), ('pencil', 'fas'), ('terjangkau', 'fas'), ('disodium', 'fas'), ('hamil', 'fas'), ('adshoptestimoni', 'fas'), ('diumumkan', 'non'), ('ekstrim', 'fas'), ('rayon', 'fas'), ('beruntun', 'non'), ('ngerusak', 'fas'), ('bolu', 'non'), ('weddinganniversary', 'non'), ('kiriii', 'fas'), ('kuehias', 'non'), ('pengelupasan', 'fas'), ('siapkan', 'non'), ('keempat', 'fas'), ('direquest', 'non'), ('engagementcupcake', 'non'), ('testimoniall', 'fas'), ('jazakillah', 'fas'), ('sayang', 'fas'), ('farewellcake', 'non'), ('jualdessert', 'non'), ('coloring', 'non'), ('mantap', 'fas'), ('valentinogaravani', 'fas'), ('lampung', 'fas'), ('jar', 'fas'), ('still', 'fas'), ('creamwanita', 'fas'), ('elener', 'fas'), ('peliharalah', 'fas'), ('iman', 'fas'), ('bc', 'fas'), ('sabarr', 'fas'), ('kuki', 'fas'), ('komennya', 'fas'), ('sissy', 'fas'), ('pengawasan', 'fas'), ('ajaib', 'fas'), ('kemaren', 'fas'), ('cupid', 'fas'), ('pokonyaaaa', 'fas'), ('precious', 'fas'), ('lengket', 'non'), ('introspeksi', 'fas'), ('fakeeyelashesnfrom', 'fas'), ('didagu', 'fas'), ('kelewatan', 'fas'), ('selfie', 'fas'), ('bestteamever', 'fas'), ('serapi', 'fas'), ('kecantikan', 'fas'), ('minimum', 'non'), ('smuaaaa', 'fas'), ('yaaaiiiyyy', 'fas'), ('coklat', 'non'), ('hijabformyhijabshop', 'fas'), ('therapy', 'fas'), ('rangga', 'non'), ('repeating', 'non'), ('awet', 'fas'), ('you', 'non'), ('pemenang', 'non'), ('jersey', 'non'), ('pobangkok', 'fas'), ('callie', 'non'), ('neat', 'fas'), ('wideshawl', 'fas'), ('gula', 'fas'), ('alergi', 'fas'), ('hairtools', 'fas'), ('year', 'fas'), ('jorr', 'fas'), ('temannya', 'fas'), ('cantikalami', 'fas'), ('antusiasnya', 'fas'), ('ohsospecial', 'fas'), ('minal', 'non'), ('makeupartistsbandung', 'fas'), ('shields', 'fas'), ('klien', 'fas'), ('auragloww', 'fas'), ('harrypottercupcakes', 'non'), ('jualmmg', 'fas'), ('greencupcake', 'non'), ('disekitar', 'fas'), ('membayar', 'fas'), ('renungkan', 'fas'), ('totalan', 'fas'), ('sang', 'fas'), ('cardigan', 'fas'), ('kekuatannya', 'fas'), ('champagne', 'fas'), ('fakeeyelashes', 'fas'), ('palette', 'fas'), ('disneyfrozen', 'non'), ('baguuss', 'fas'), ('kmarin', 'fas'), ('betaah', 'non'), ('efektif', 'non'), ('youuuuu', 'fas'), ('engagementcupcakes', 'non'), ('items', 'fas'), ('brushguard', 'fas'), ('lips', 'non'), ('pashyds', 'fas'), ('favorit', 'non'), ('madelaine', 'non'), ('diantar', 'fas'), ('latesurprise', 'non'), ('dimesin', 'fas'), ('flareskirt', 'fas'), ('brushcase', 'fas'), ('mmu', 'fas'), ('naja', 'non'), ('socke', 'fas'), ('aulawiyyat', 'fas'), ('michael', 'fas'), ('bra', 'non'), ('hermes', 'fas'), ('ootdindo', 'fas'), ('dlunya', 'fas'), ('lashes', 'fas'), ('desiana', 'fas'), ('lurus', 'fas'), ('eyeliners', 'fas'), ('walid', 'fas'), ('enak', 'fas'), ('japri', 'non'), ('newbieinkitchen', 'non'), ('gedee', 'fas'), ('komen', 'non'), ('famous', 'fas'), ('yihaaa', 'fas'), ('sunflower', 'fas'), ('divine', 'fas'), ('sukses', 'non'), ('lgi', 'fas'), ('bertambahnya', 'fas'), ('bermotif', 'fas'), ('pengertiannya', 'non'), ('sleekcandy', 'fas'), ('moistcupcakes', 'non'), ('tubuhnya', 'fas'), ('numpang', 'fas'), ('max', 'non'), 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('makeupdisurabaya', 'fas'), ('lalai', 'fas'), ('clozetteid', 'fas'), ('upload', 'fas'), ('thailand', 'fas'), ('jualkueultah', 'non'), ('so', 'non'), ('lowongan', 'fas'), ('perhiasan', 'fas'), ('yaampun', 'fas'), ('gigi', 'fas'), ('admin', 'fas'), ('jualhijabmurah', 'fas'), ('penggaris', 'fas'), ('sampaiii', 'fas'), ('ambil', 'fas'), ('blackcoffee', 'non'), ('malas', 'fas'), ('truestedolshop', 'non'), ('imagine', 'fas'), ('makasiii', 'fas'), ('bike', 'non'), ('berkurangnya', 'fas'), ('simplystore', 'fas'), ('dahlan', 'fas'), ('masyalah', 'fas'), ('berantakan', 'fas'), ('segar', 'fas'), ('lovely', 'non'), ('anha', 'fas'), ('bsyukur', 'fas'), ('akun', 'fas'), ('peak', 'fas'), ('lagiiiiii', 'fas'), ('muchhhhh', 'fas'), ('moto', 'fas'), ('cheers', 'fas'), ('inc', 'non'), ('pembungkus', 'fas'), ('raniainstantshawl', 'fas'), ('cupcakeporn', 'non'), ('bf', 'fas'), ('potok', 'fas'), ('orchard', 'fas'), ('alhamdulillah', 'non'), ('tas', 'non'), ('school', 'non'), ('suhu', 'non'), ('batmancake', 'non'), ('claradevi', 'fas'), ('pelajaran', 'fas'), ('d', 'fas'), ('supermancupcakes', 'non'), ('collection', 'fas'), ('lenongannya', 'fas'), ('atsar', 'fas'), ('mencintai', 'fas'), ('ketinggian', 'non'), ('cateyes', 'fas'), ('botolnya', 'fas'), ('makeupbyainun', 'fas'), ('catokanmurah', 'fas'), ('inside', 'non'), ('khimar', 'fas'), ('pingin', 'non'), ('dipilih', 'non'), ('psp', 'non'), ('lp', 'fas'), ('kueenak', 'non'), ('pasang', 'fas'), ('finish', 'fas'), ('epic', 'non'), ('quick', 'fas'), ('mempermudah', 'fas'), ('tiramisuuu', 'non'), ('lets', 'non'), ('alhamdulillah', 'fas'), ('matrimony', 'fas'), ('bingitss', 'fas'), ('customerku', 'fas'), ('colour', 'fas'), ('dkirim', 'fas'), ('mr', 'non'), ('cause', 'fas'), ('makeupartistjakarta', 'fas'), ('mattebrown', 'fas'), ('serang', 'non'), ('saranin', 'fas'), ('kelangsungan', 'fas'), ('transfer', 'fas'), ('crepe', 'fas'), ('bentuknya', 'non'), ('mengalir', 'fas'), ('stand', 'fas'), ('dramatic', 'fas'), ('tabrani', 'fas'), ('cartoon', 'non'), ('ngadain', 'fas'), ('surabaya', 'non'), ('people', 'fas'), ('bergo', 'fas'), ('cari', 'fas'), ('muluk', 'fas'), ('jarum', 'fas'), ('button', 'non'), ('lhooo', 'fas'), ('golf', 'non'), ('sehaattt', 'fas'), ('silaturahim', 'fas'), ('keceh', 'fas'), ('osi', 'non'), ('terpisah', 'fas'), ('everyday', 'non'), ('sofy', 'fas'), ('shabbycupcakes', 'non'), ('dri', 'fas'), ('boy', 'fas'), ('barneythedinosaur', 'non'), ('thebalm', 'fas'), ('gratiiiiss', 'fas'), ('tengkis', 'fas'), ('nyusul', 'fas'), ('birthdaygirl', 'non'), ('mmgel', 'fas'), ('senengnya', 'fas'), ('numpukk', 'fas'), ('flawless', 'fas'), ('pesan', 'non'), ('vakum', 'fas'), ('bingkisanunik', 'non'), ('disuruh', 'fas'), ('hijablook', 'fas'), ('jagalah', 'fas'), ('senengnya', 'non'), ('ilovebrush', 'fas'), ('herbal', 'fas'), ('jualanbulumata', 'fas'), ('cutratumeyriska', 'non'), ('kahfi', 'fas'), ('sbg', 'fas'), ('spacious', 'fas'), ('testimonialviolettaroom', 'fas'), ('narendra', 'non'), ('belajar', 'fas'), ('engga', 'fas'), ('syaratnya', 'fas'), ('creamherbal', 'fas'), ('nyuciii', 'fas'), ('pomade', 'fas'), ('makeupartistbekasi', 'fas'), ('hancur', 'non'), ('guardnya', 'fas'), ('seas', 'fas'), ('bon', 'fas'), ('picked', 'fas'), ('maret', 'fas'), ('ternyta', 'fas'), ('cantikkkk', 'fas'), ('pengusaha', 'fas'), ('amateur', 'fas'), ('eyeshadowlovers', 'fas'), ('dian', 'fas'), ('tia', 'fas'), ('ll', 'non'), ('hadiah', 'fas'), ('seleting', 'fas'), ('winered', 'fas'), ('pembelian', 'fas'), ('pesanan', 'non'), ('aa', 'non'), ('pikiran', 'fas'), ('mutsreview', 'fas'), ('pemukiman', 'fas'), ('sheniaaraminta', 'fas'), ('alarm', 'fas'), ('kecepatan', 'non'), ('kipaaaa', 'fas'), ('peace', 'fas'), ('friday', 'fas'), ('buketbunga', 'non'), ('jabotabotabek', 'non'), ('alumina', 'fas'), ('sekat', 'fas'), ('berhubung', 'fas'), ('mantaaappp', 'fas'), ('peringatan', 'fas'), ('loyal', 'fas'), ('jetii', 'fas'), ('sisnatalie', 'fas'), ('double', 'fas'), ('various', 'fas'), ('temvus', 'fas'), ('facebrush', 'fas'), ('disenanginya', 'fas'), ('cardy', 'fas'), ('terimakasih', 'fas'), ('sukses', 'fas'), ('vx', 'fas'), ('macbrushes', 'fas'), ('makeupholic', 'fas'), ('haiii', 'fas'), ('bhan', 'fas'), ('temporary', 'fas'), ('memecah', 'fas'), ('ijin', 'fas'), ('cakesdaily', 'non'), ('pesta', 'fas'), ('give', 'fas'), ('creampria', 'fas'), ('lagirl', 'fas'), ('baekkk', 'fas'), ('iklanin', 'non'), ('uemura', 'fas'), ('panda', 'non'), ('mempercayakan', 'fas'), ('berbusa', 'fas'), ('fak', 'fas'), ('bit', 'fas'), ('melitta', 'non'), ('sbgian', 'fas'), ('des', 'fas'), ('dalil', 'fas'), ('cupcakesnya', 'non'), ('frenchmacaron', 'non'), ('jualjarcake', 'non'), ('kosmetik', 'fas'), ('dipasang', 'fas'), ('flowercrown', 'fas'), ('bored', 'fas'), ('can', 'non'), ('akupun', 'fas'), ('asikkk', 'fas'), ('komponen', 'non'), ('cobain', 'fas'), ('regardless', 'fas'), ('ekpedisi', 'fas'), ('veggie', 'non'), ('wktunya', 'fas'), ('fireman', 'non'), ('keju', 'fas'), ('life', 'fas'), ('infolowkerjogja', 'fas'), ('tutorial', 'non'), ('kakak', 'non'), ('pigmented', 'fas'), ('megadeth', 'non'), ('non', 'fas'), ('am', 'fas'), ('ditambah', 'fas'), ('lhoh', 'fas'), ('black', 'fas'), ('kuasss', 'fas'), ('fa', 'fas'), ('dancing', 'fas'), ('crease', 'fas'), ('segaaar', 'non'), ('limited', 'non'), ('payment', 'fas'), ('h', 'non'), ('panasnya', 'fas'), ('cuci', 'non'), ('fanpage', 'fas'), ('concealer', 'fas'), ('anggi', 'non'), ('likeslikes', 'fas'), ('itcake', 'non'), ('smuanya', 'fas'), ('yours', 'fas'), ('mmbuang', 'fas'), ('taat', 'fas'), ('sampel', 'fas'), ('kalah', 'non'), ('ayam', 'non'), ('alhamdulilahhh', 'fas'), ('cheddar', 'non'), ('legostarwars', 'non'), ('pengusahamuslimah', 'fas'), ('choice', 'non'), ('hellokittyforever', 'fas'), ('quad', 'fas'), ('it', 'fas'), ('toko', 'fas'), ('shower', 'non'), ('garskin', 'fas'), ('heningkan', 'fas'), ('tee', 'fas'), ('makasii', 'fas'), ('easy', 'fas'), ('khimarsyarie', 'fas'), ('menyengat', 'fas'), ('patokan', 'non'), ('meregenerasi', 'fas'), ('luka', 'fas'), ('janji', 'fas'), ('frenchmacarons', 'non'), ('kawan', 'fas'), ('serut', 'fas'), ('vonny', 'fas'), ('beyblade', 'non'), ('ilaikan', 'fas'), ('frozendisney', 'non'), ('sejumput', 'non'), ('deeear', 'fas'), ('pajerosport', 'non'), ('exotic', 'fas'), ('tangannya', 'non'), ('twin', 'non'), ('pelangi', 'fas'), ('kematian', 'fas'), ('makeupart', 'fas'), ('hihihii', 'fas'), ('butter', 'non'), ('jmpcomm', 'fas'), ('sari', 'fas'), ('bigben', 'non'), ('smoochy', 'fas'), ('bikecake', 'non'), ('rok', 'fas'), ('indonesianidol', 'non'), ('suksessss', 'fas'), ('pm', 'fas'), ('barcelona', 'non'), ('basic', 'fas'), ('taruh', 'non'), ('nine', 'fas'), ('nina', 'fas'), ('bebeb', 'fas'), ('lemoncake', 'non'), ('jelaa', 'fas'), ('hepimakeup', 'fas'), ('butterflies', 'fas'), ('primers', 'fas'), ('informasinya', 'fas'), ('fb', 'fas'), ('adreena', 'non'), ('tibby', 'non'), ('peanutbuttercake', 'non'), ('caketingkat', 'non'), ('hoghwarts', 'non'), ('loyang', 'non'), ('jadwal', 'fas'), ('sponsor', 'fas'), ('dioles', 'non'), ('jualmurmer', 'fas'), ('sukaa', 'fas'), ('sempet', 'fas'), ('lembek', 'non'), ('fill', 'fas'), ('main', 'non'), ('flamingo', 'fas'), ('nasihatindah', 'fas'), ('graduationcupcake', 'non'), ('hihiiii', 'fas'), ('higs', 'fas'), ('pasangan', 'fas'), ('undian', 'fas'), ('application', 'fas'), ('step', 'fas'), ('eksperimen', 'non'), ('thankk', 'fas'), ('shoponline', 'fas'), ('rulespojanuari', 'fas'), ('collar', 'fas'), ('flowers', 'fas'), ('ditanyain', 'non'), ('quran', 'non'), ('blm', 'fas'), ('giveawayindo', 'non'), ('cupcakeart', 'non'), ('wajib', 'fas'), ('paula', 'fas'), ('surprise', 'fas'), ('vonnyyo', 'fas'), ('copast', 'fas'), ('farenchan', 'fas'), ('small', 'fas'), ('si', 'non'), ('mut', 'fas'), ('kenalan', 'fas'), ('cmon', 'fas'), ('areas', 'fas'), ('barokah', 'fas'), ('rusuh', 'fas'), ('disesali', 'fas'), ('actionchugger', 'non'), ('brushnyaaa', 'fas'), ('wisdom', 'non'), ('saatt', 'fas'), ('seru', 'non'), ('buktikan', 'fas'), ('shoes', 'fas'), ('pogba', 'non'), ('benerkan', 'fas'), ('cetarr', 'fas'), ('kalila', 'non'), ('kw', 'fas'), ('gni', 'fas'), ('ditanggung', 'non'), ('addict', 'fas'), ('peasy', 'fas'), ('contain', 'fas'), ('project', 'fas'), ('skinfood', 'fas'), ('eyekoeyeshadow', 'fas'), ('liner', 'fas'), ('four', 'fas'), ('tks', 'fas'), ('ayu', 'non'), ('discontinue', 'fas'), ('dihubungi', 'fas'), ('tinkerbell', 'non'), ('wash', 'fas'), ('goodmorning', 'fas'), ('jorok', 'fas'), ('kadolamaran', 'non'), ('mengatasi', 'fas'), ('smoke', 'fas'), ('jualanpashmina', 'fas'), ('dirapiin', 'fas'), ('muchhh', 'fas'), ('marlboro', 'non'), ('supplier', 'fas'), ('gendut', 'fas'), ('sharing', 'fas'), ('bersambung', 'fas'), ('chocopeanut', 'non'), ('tribal', 'fas'), ('menghubungi', 'non'), ('hw', 'fas'), ('ngabarin', 'fas'), ('brenti', 'fas'), ('nia', 'fas'), ('dtanyain', 'fas'), ('done', 'fas'), ('babyliss', 'fas'), ('dioven', 'non'), ('ibunya', 'non'), ('diprodujsi', 'fas'), ('hellokittysaga', 'fas'), ('asalan', 'fas'), ('nobody', 'non'), ('mila', 'non'), ('btw', 'fas'), ('bebas', 'fas'), ('caprylic', 'fas'), ('puluhan', 'non'), ('think', 'fas'), ('smangattt', 'fas'), ('swipe', 'fas'), ('rizky', 'fas'), ('rafelia', 'fas'), ('indah', 'fas'), ('krna', 'fas'), ('melepaskan', 'fas'), ('ayobaking', 'non'), ('widyastoto', 'non'), ('kabuuurr', 'fas'), ('shadow', 'fas'), ('gnache', 'non'), ('dipercaya', 'fas'), ('jingle', 'fas'), ('zara', 'fas'), ('lipatan', 'fas'), ('one', 'fas'), ('msuk', 'fas'), ('suhunya', 'non'), ('years', 'non'), ('ethylhexylglycerin', 'fas'), ('kulitmu', 'non'), ('makeupaddicted', 'fas'), ('terbuktii', 'fas'), ('juara', 'non'), ('square', 'fas'), ('sista', 'fas'), ('sinau', 'fas'), ('persedian', 'fas'), ('goes', 'non'), ('whatsapp', 'fas'), ('rotation', 'fas'), ('lingkara', 'fas'), ('pegawaii', 'fas'), ('pusatauraglow', 'fas'), ('naaudzibillahhimindzalik', 'fas'), ('hatinya', 'fas'), ('brother', 'non'), ('feel', 'non'), ('brazilian', 'fas'), ('bestbeautiful', 'fas'), ('swooo', 'fas'), ('bingitttt', 'fas'), ('tarakan', 'fas'), ('afternoon', 'non'), ('super', 'fas'), ('cucoookk', 'fas'), ('merespon', 'fas'), ('beige', 'fas'), ('terbentuk', 'fas'), ('panas', 'fas'), ('kebanggaan', 'non'), ('tea', 'fas'), ('ribbon', 'fas'), ('explosion', 'fas'), ('droolmixingpalette', 'fas'), ('botol', 'fas'), ('boss', 'non'), ('aswari', 'fas'), ('shaleh', 'fas'), ('justneverenough', 'fas'), ('jualanrambut', 'fas'), ('keikhlasan', 'fas'), ('were', 'non'), ('working', 'fas'), ('iyaa', 'non'), ('berwarna', 'non'), ('thankies', 'fas'), ('berqurbqn', 'fas'), ('teyuus', 'non'), ('just', 'non'), ('speed', 'non'), ('katecosmetic', 'fas'), ('menyatu', 'fas'), ('minutes', 'fas'), ('eotd', 'fas'), ('bikini', 'non'), ('mochi', 'non'), ('bigsale', 'fas'), ('storm', 'fas'), ('menyiksa', 'fas'), ('cardiganmurah', 'fas'), ('zipper', 'fas'), ('ayoooo', 'fas'), ('kerren', 'fas'), ('tercinta', 'fas'), ('fashionista', 'fas'), ('newbieinthekitchen', 'non'), ('ilovemakeup', 'fas'), ('tccopheryl', 'fas'), ('mixingpalette', 'fas'), ('myself', 'fas'), ('rejekii', 'fas'), ('hmm', 'fas'), ('sooooo', 'fas'), ('minyu', 'non'), ('motoinnya', 'fas'), ('tarik', 'fas'), ('page', 'fas'), ('tenis', 'non'), ('manis', 'fas'), ('secret', 'fas'), ('relatif', 'fas'), ('real', 'non'), ('ngapain', 'fas'), ('kemeja', 'non'), ('pastii', 'fas'), ('havana', 'fas'), ('kehutanan', 'fas'), ('banettt', 'fas'), ('merata', 'fas'), ('itali', 'fas'), ('haircliplurus', 'fas'), ('sejenis', 'fas'), ('diberitahuan', 'fas'), ('cakeep', 'non'), ('shadung', 'fas'), ('ikutannnn', 'fas'), ('katalogpojanuari', 'fas'), ('sel', 'fas'), ('her', 'non'), ('sonta', 'non'), ('princessanna', 'non'), ('jualcupcakes', 'non'), ('melayani', 'non'), ('os', 'non'), ('panada', 'non'), ('dormant', 'fas'), ('surrounded', 'non'), ('pabrik', 'fas'), ('maxi', 'fas'), ('yeeiiiyyyy', 'fas'), ('skipper', 'non'), ('linesticker', 'non'), ('makeuptoolshop', 'fas'), ('hellokittycupcake', 'non'), ('beriman', 'fas'), ('ilaihi', 'fas'), ('jl', 'fas'), ('selfmade', 'fas'), ('expertbrush', 'fas'), ('ijin', 'non'), ('faith', 'fas'), ('ddae', 'fas'), ('choco', 'non'), ('sedih', 'fas'), ('konsisten', 'non'), ('kewl', 'non'), ('riassurabaya', 'fas'), ('pregnancy', 'non'), ('haey', 'fas'), ('inner', 'fas'), ('dilanjut', 'fas'), ('second', 'fas'), ('subuh', 'non'), ('umeko', 'fas'), ('wakeupandmakeup', 'fas'), ('crocus', 'fas'), ('traincupcake', 'non'), ('rapunzelcupcake', 'non'), ('dibentang', 'fas'), ('such', 'non'), ('pocoyo', 'non'), ('moggo', 'fas'), ('keju', 'non'), ('hehe', 'non'), ('rqnia', 'fas'), ('blh', 'fas'), ('amrezy', 'fas'), ('iphone', 'fas'), ('goesgreen', 'non'), ('pokoknya', 'fas'), ('monggo', 'fas'), ('agung', 'fas'), ('butterfly', 'fas'), ('berukuran', 'fas'), ('posting', 'fas'), ('barneycupcakes', 'non'), ('motorsport', 'non'), ('kesayangan', 'fas'), ('menghalaginya', 'fas'), ('teksturnya', 'fas'), ('amsainaa', 'fas'), ('berdosa', 'fas'), ('private', 'fas'), ('chips', 'non'), ('garudaindonesia', 'non'), ('ngrjain', 'fas'), ('menewaskan', 'fas'), ('minicupcakes', 'non'), ('memperkuat', 'fas'), ('number', 'fas'), ('faktor', 'non'), ('sunsilk', 'fas'), ('bright', 'fas'), ('jonathan', 'non'), ('longgar', 'fas'), ('testiblastacake', 'non'), ('edible', 'non'), ('longgaarr', 'fas'), ('reef', 'fas'), ('tllu', 'non'), ('dehh', 'fas'), ('rosecupcakes', 'non'), ('tease', 'fas'), ('plg', 'non'), ('pelit', 'fas'), ('keke', 'non'), ('olifia', 'fas'), ('fondantrose', 'non'), ('merah', 'fas'), ('hatimu', 'fas'), ('dtengah', 'fas'), ('diboandfriends', 'non'), ('smoothie', 'non'), ('terbaikmu', 'fas'), ('arah', 'non'), ('membenci', 'fas'), ('jualminutemiraclegel', 'fas'), ('pahala', 'fas'), ('sertakan', 'fas'), ('senyuman', 'fas'), ('konturing', 'fas'), ('mengubah', 'fas'), ('makeoverid', 'fas'), ('didepan', 'fas'), ('flowery', 'non'), ('saldo', 'fas'), ('bw', 'fas'), ('fuchsia', 'fas'), ('serasa', 'fas'), ('emboss', 'fas'), ('imran', 'fas'), ('pembangun', 'fas'), ('janjiii', 'fas'), ('nongki', 'fas'), ('ditabahkan', 'fas'), ('request', 'fas'), ('balm', 'fas'), ('xman', 'non'), ('muchhhh', 'fas'), ('dibesarkan', 'fas'), ('xjam', 'non'), ('single', 'fas'), ('outdoor', 'fas'), ('pajangan', 'non'), ('quotesbyafhj', 'fas'), ('alcohol', 'fas'), ('harclipmurah', 'fas'), ('yaaaaayyyy', 'fas'), ('alaikum', 'fas'), ('oval', 'fas'), ('xixixi', 'fas'), ('berpeci', 'fas'), ('smudge', 'fas'), ('kalimat', 'fas'), ('freshtops', 'fas'), ('makeupjungkie', 'fas'), ('travelling', 'fas'), ('natal', 'fas'), ('insyaallah', 'fas'), ('hijabsyaribrand', 'fas'), ('bombaysapphire', 'non'), ('aplikasinya', 'fas'), ('sukaaaaaaa', 'fas'), ('selulit', 'fas'), ('betebaran', 'fas'), ('sweetseventeen', 'non'), ('fuchia', 'fas'), ('diatas', 'fas'), ('memasang', 'fas'), ('skripsi', 'non'), ('kelasmakeup', 'fas'), ('kajian', 'fas'), ('istimewaaa', 'fas'), ('ask', 'non'), ('dapet', 'fas'), ('beb', 'fas'), ('wajahhh', 'fas'), ('saran', 'non'), ('ja', 'fas'), ('ice', 'non'), ('with', 'non'), ('aviator', 'non'), ('snowhite', 'fas'), ('put', 'non'), ('jelek', 'fas'), ('pangling', 'fas'), ('wrn', 'fas'), ('nupus', 'non'), ('half', 'fas'), ('bgus', 'fas'), ('slamat', 'fas'), ('filling', 'non'), ('happyweddingayu', 'fas'), ('hawa', 'fas'), ('pemasangan', 'fas'), ('animalcookies', 'non'), ('pagiiii', 'fas'), ('buyer', 'fas'), ('memperkecil', 'fas'), ('ayah', 'non'), ('vscocam', 'fas'), ('jualkuasmakeup', 'fas'), ('halal', 'non'), ('pria', 'fas'), ('seminar', 'fas'), ('gendhis', 'non'), ('sunset', 'non'), ('come', 'fas'), ('jum', 'fas'), ('cus', 'fas'), ('mixer', 'non'), ('thanks', 'fas'), ('dearrr', 'fas'), ('merawat', 'fas'), ('zat', 'fas'), ('stujuu', 'non'), ('tenang', 'non'), ('candycrushcupcakes', 'non'), ('dikasih', 'fas')]\n" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "len(labeled_posts)\n", "split = int(0.9 * len(labeled_posts))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 37 }, { "cell_type": "code", "collapsed": false, "input": [ "featuresets = [(document_features(n), cat) for (n, cat) in labeled_posts]\n", "train_set, test_set = featuresets[:split], featuresets[split:]\n", "classifier = nltk.NaiveBayesClassifier.train(train_set)\n", "print (nltk.classify.accuracy(classifier, test_set))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.718074656189\n" ] } ], "prompt_number": 38 }, { "cell_type": "code", "collapsed": false, "input": [ "classifier.show_most_informative_features(30)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Most Informative Features\n", " contain(cake) = True non : fas = 120.8 : 1.0\n", " contain(enak) = True non : fas = 46.5 : 1.0\n", " contain(kosmetik) = True fas : non = 45.9 : 1.0\n", " contain(family) = True non : fas = 45.0 : 1.0\n", " contain(cuci) = True fas : non = 40.3 : 1.0\n", " contain(set) = True fas : non = 29.3 : 1.0\n", " contain(box) = True fas : non = 28.0 : 1.0\n", " contain(pasang) = True fas : non = 26.1 : 1.0\n", " contain(black) = True fas : non = 23.5 : 1.0\n", " contain(isi) = True fas : non = 21.3 : 1.0\n", " contain(cute) = True non : fas = 18.7 : 1.0\n", " contain(mba) = True non : fas = 16.9 : 1.0\n", " contain(always) = True fas : non = 16.1 : 1.0\n", " contain(pemenang) = True non : fas = 14.3 : 1.0\n", " contain(instabeauty) = True non : fas = 14.3 : 1.0\n", " contain(brown) = True fas : non = 12.9 : 1.0\n", " contain(using) = True fas : non = 12.6 : 1.0\n", " contain(best) = True fas : non = 11.8 : 1.0\n", " contain(lho) = True non : fas = 11.7 : 1.0\n", " contain(boy) = True non : fas = 10.8 : 1.0\n", " contain(happy) = True non : fas = 10.7 : 1.0\n", " contain(up) = True fas : non = 9.8 : 1.0\n", " contain(mix) = True fas : non = 9.8 : 1.0\n", " contain(wedding) = True non : fas = 9.6 : 1.0\n", " contain(art) = True non : fas = 9.6 : 1.0\n", " contain(makeup) = True fas : non = 9.1 : 1.0\n", " contain(powder) = True fas : non = 8.6 : 1.0\n", " contain(m) = True fas : non = 8.6 : 1.0\n", " contain(aman) = True fas : non = 8.5 : 1.0\n", " contain(beauty) = True fas : non = 8.5 : 1.0\n" ] } ], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [ "# Count fashion posts in #onlineshopbali\n", "count_fas = 0\n", "count_posts = 0\n", "f = open('data_cleaned_bali.txt', 'r')\n", "for line in f.readlines():\n", " cap_array = line.split('|')\n", " text = cap_array[6]\n", " if classifier.classify(document_features(text)) == 'fas':\n", " count_fas += 1\n", " count_posts += 1\n", "\n", "f.close()\n", "print count_posts, count_fas" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "30133 29985\n" ] } ], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "# Count fashion posts in #onlineshopjkt\n", "count_fas = 0\n", "count_posts = 0\n", "f = open('data_cleaned_jkt.txt', 'r')\n", "for line in f.readlines():\n", " cap_array = line.split('|')\n", " text = cap_array[6]\n", " if classifier.classify(document_features(text)) == 'fas':\n", " count_fas += 1\n", " count_posts += 1\n", "\n", "f.close()\n", "print count_posts, count_fas" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "30161 30022\n" ] } ], "prompt_number": 28 }, { "cell_type": "code", "collapsed": false, "input": [ "# Count fashion posts in #onlineshopbdg\n", "count_fas = 0\n", "count_posts = 0\n", "f = open('data_cleaned_bdg.txt', 'r')\n", "for line in f.readlines():\n", " cap_array = line.split('|')\n", " text = cap_array[6]\n", " if classifier.classify(document_features(text)) == 'fas':\n", " count_fas += 1\n", " count_posts += 1\n", "\n", "f.close()\n", "print count_posts, count_fas" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "30404 30306\n" ] } ], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [ "# Count fashion posts in #onlineshopjogja\n", "count_fas = 0\n", "count_posts = 0\n", "f = open('data_cleaned_jogja.txt', 'r')\n", "for line in f.readlines():\n", " cap_array = line.split('|')\n", " text = cap_array[6]\n", " if classifier.classify(document_features(text)) == 'fas':\n", " count_fas += 1\n", " count_posts += 1\n", "\n", "f.close()\n", "print count_posts, count_fas" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "37010 36897\n" ] } ], "prompt_number": 30 } ], "metadata": {} } ] }