{
"cells": [
{
"cell_type": "markdown",
"id": "80bf830b",
"metadata": {},
"source": [
" # Final Project: Leah Fitzgerald"
]
},
{
"cell_type": "markdown",
"id": "c0a6d14a",
"metadata": {},
"source": [
"The purpose of my final project analysis is to see what show viewers on Twitter like more, Jeopardy! or Wheel of Fortune. One motivation that has driven me in the creation of this analysis is a debate between a few of my roommates and Erica and I. Ms. Erica and I believe that Jeopardy! is a better show for numerous reasons. Our roommates disagree. With this analysis, I can show them step-by-step what users are saying on Twitter. \n",
"\n",
"I will start my analysis of tweets regarding Jeopardy! and Wheel of Fortune by importing the libraries necessary. The hypothesis I am testing in this analysis is: Users of Twitter will generally favor and say more positive things about Jeopardy! vs. Wheel of Fortune. The audience of my analysis would be Jeopardy! and Wheel of Fortune fans, and viewers of both shows. \n",
"\n",
"My conclusions will give me insight as to whether Twitter users are more positively talking about either WOF or Jeopardy, and also specifics about what people have to say about the hosts. So first, understanding which show is better received based on the tweets collected. I have done this by pulling data about each game and the hosts into 2 files.\n",
"\n",
" The data used is all coming from the Twitter API that has pulled tweets from the past 7 days. It provides personal experiences and perspectives from a wide variety of users on Twitter."
]
},
{
"cell_type": "markdown",
"id": "fd7fda34",
"metadata": {},
"source": [
"### Data Collection"
]
},
{
"cell_type": "markdown",
"id": "de9c3188",
"metadata": {},
"source": [
"I have collected 600 samples of tweets regarding Jeopardy and 600 tweets regarding Wheel of Fortune. "
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "bb2d2236",
"metadata": {},
"outputs": [],
"source": [
"import requests\n",
"import pandas as pd\n",
"import urllib\n",
"import json"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "24a69bd9",
"metadata": {},
"outputs": [],
"source": [
"from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "39d13cc0",
"metadata": {},
"outputs": [],
"source": [
"ls"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "9e6021bf",
"metadata": {},
"outputs": [],
"source": [
"bearer_token = pd.read_csv('bearer_token.txt', header = 0)"
]
},
{
"cell_type": "markdown",
"id": "316453d3",
"metadata": {},
"source": [
"I start by asking it to read my bearer_token text file. This contains the token I need to get data back from the Twitter API"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c6135c25",
"metadata": {},
"outputs": [],
"source": [
"bearer_token['Bearer_token'].iloc[0]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "308eba77",
"metadata": {},
"outputs": [],
"source": [
"header = {'Authorization' : 'Bearer {}'.format(bearer_token['Bearer_token'].iloc[0])}"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "0f824e5a",
"metadata": {},
"outputs": [],
"source": [
"endpoint_url = 'https://api.twitter.com/2/tweets/search/recent'"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "95ce32d7",
"metadata": {},
"outputs": [],
"source": [
"query = urllib.parse.quote('#Jeopardy OR #KenJennings OR @KenJennings OR @MissMayim OR #MayimBialik OR @Jeopardy lang:en')"
]
},
{
"cell_type": "markdown",
"id": "e6792905",
"metadata": {},
"source": [
"Here I am creating a query to parse the topics and language for my url. "
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "2a85541a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'%23Jeopardy%20OR%20%23KenJennings%20OR%20%40KenJennings%20OR%20%40MissMayim%20OR%20%23MayimBialik%20OR%20%40Jeopardy%20lang%3Aen'"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "aab08f18",
"metadata": {},
"outputs": [],
"source": [
"tweet_fields = 'public_metrics,created_at,lang,possibly_sensitive,attachments,source'"
]
},
{
"cell_type": "markdown",
"id": "1d06c872",
"metadata": {},
"source": [
"I am declaring the tweet fields I would like to see from Twitter."
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "71de45c1",
"metadata": {},
"outputs": [],
"source": [
"my_api_url = endpoint_url + '?query={}&tweet.fields={}'.format(query, tweet_fields)"
]
},
{
"cell_type": "markdown",
"id": "3ba08719",
"metadata": {},
"source": [
"Here I am consolidating my url with the query."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1921e6c9",
"metadata": {},
"outputs": [],
"source": [
"my_api_url"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "ce3c3b30",
"metadata": {},
"outputs": [],
"source": [
"expansions = 'author_id'"
]
},
{
"cell_type": "markdown",
"id": "4c6a9ec6",
"metadata": {},
"source": [
"I added author_id as an expansion to get more information."
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "ff158998",
"metadata": {},
"outputs": [],
"source": [
"url = endpoint_url + '?query={}&tweet.fields={}'.format(query, tweet_fields)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "506487d7",
"metadata": {},
"outputs": [],
"source": [
"url_expansions = endpoint_url + '?query={}&max_results=100&tweet.fields={}&expansions={}&user.fields={}'.format(query, tweet_fields, expansions, 'username')"
]
},
{
"cell_type": "markdown",
"id": "c1dacc86",
"metadata": {},
"source": [
"Here I am specifying what I would like from the twitter data. Specifically max results of 100 tweets, tweet fields, expansions, and user fields. "
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "b924b2f0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'https://api.twitter.com/2/tweets/search/recent?query=%23Jeopardy%20OR%20%23KenJennings%20OR%20%40KenJennings%20OR%20%40MissMayim%20OR%20%23MayimBialik%20OR%20%40Jeopardy%20lang%3Aen&max_results=100&tweet.fields=public_metrics,created_at,lang,possibly_sensitive,attachments,source&expansions=author_id&user.fields=username'"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"url_expansions"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "7ba72289",
"metadata": {},
"outputs": [],
"source": [
"response_1 = requests.request(\"GET\", url_expansions, headers = header)"
]
},
{
"cell_type": "markdown",
"id": "968f6fbb",
"metadata": {},
"source": [
"Here I am asking Twitter to answer my request using \"GET\" with my url. "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7b3fa5f0",
"metadata": {},
"outputs": [],
"source": [
"response_1.text"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "e743e67b",
"metadata": {},
"outputs": [],
"source": [
"response_1_dict = json.loads(response_1.text)"
]
},
{
"cell_type": "markdown",
"id": "486448ed",
"metadata": {},
"source": [
"Loading Twitter data into json"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "e9cd88a2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys(['data', 'includes', 'meta'])"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"response_1_dict.keys()"
]
},
{
"cell_type": "markdown",
"id": "e36db869",
"metadata": {},
"source": [
"This tells me what I can ask the API to show me. "
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "f7313493",
"metadata": {},
"outputs": [],
"source": [
"my_df = pd.DataFrame(response_1_dict['data'])"
]
},
{
"cell_type": "markdown",
"id": "36241312",
"metadata": {},
"source": [
"I am creating a data frame with the 'data' key."
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "fd3e4d8f",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" public_metrics \n",
" id \n",
" author_id \n",
" text \n",
" created_at \n",
" possibly_sensitive \n",
" lang \n",
" source \n",
" attachments \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" {'retweet_count': 0, 'reply_count': 0, 'like_c... \n",
" 1469055869908312066 \n",
" 31590111 \n",
" @JenniferBurt @TheDudeInSF @missmayim You are ... \n",
" 2021-12-09T21:26:27.000Z \n",
" False \n",
" en \n",
" Twitter Web App \n",
" NaN \n",
" \n",
" \n",
" 1 \n",
" {'retweet_count': 0, 'reply_count': 0, 'like_c... \n",
" 1469055781618298882 \n",
" 2479845637 \n",
" #Jeopardy! fans are obsessed with this contest... \n",
" 2021-12-09T21:26:06.000Z \n",
" False \n",
" en \n",
" SocialFlow \n",
" {'media_keys': ['3_1469055779160403974']} \n",
" \n",
" \n",
" 2 \n",
" {'retweet_count': 0, 'reply_count': 0, 'like_c... \n",
" 1469055523379154955 \n",
" 1318277923 \n",
" @Jeopardy Does anyone else think he looks a lo... \n",
" 2021-12-09T21:25:05.000Z \n",
" False \n",
" en \n",
" Twitter Web App \n",
" NaN \n",
" \n",
" \n",
" 3 \n",
" {'retweet_count': 80, 'reply_count': 0, 'like_... \n",
" 1469055109342638092 \n",
" 28445420 \n",
" RT @KenJennings: Delighted that @missmayim and... \n",
" 2021-12-09T21:23:26.000Z \n",
" False \n",
" en \n",
" Twitter for iPhone \n",
" NaN \n",
" \n",
" \n",
" 4 \n",
" {'retweet_count': 270, 'reply_count': 0, 'like... \n",
" 1469055053864525836 \n",
" 1328060923244933120 \n",
" RT @peterbelayneh: የ #TDF ጀግንነት ታሪክ ላይ ተፅፏል \\n... \n",
" 2021-12-09T21:23:13.000Z \n",
" False \n",
" und \n",
" Twitter for Android \n",
" {'media_keys': ['7_1468804403410857986']} \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" public_metrics id \\\n",
"0 {'retweet_count': 0, 'reply_count': 0, 'like_c... 1469055869908312066 \n",
"1 {'retweet_count': 0, 'reply_count': 0, 'like_c... 1469055781618298882 \n",
"2 {'retweet_count': 0, 'reply_count': 0, 'like_c... 1469055523379154955 \n",
"3 {'retweet_count': 80, 'reply_count': 0, 'like_... 1469055109342638092 \n",
"4 {'retweet_count': 270, 'reply_count': 0, 'like... 1469055053864525836 \n",
"\n",
" author_id text \\\n",
"0 31590111 @JenniferBurt @TheDudeInSF @missmayim You are ... \n",
"1 2479845637 #Jeopardy! fans are obsessed with this contest... \n",
"2 1318277923 @Jeopardy Does anyone else think he looks a lo... \n",
"3 28445420 RT @KenJennings: Delighted that @missmayim and... \n",
"4 1328060923244933120 RT @peterbelayneh: የ #TDF ጀግንነት ታሪክ ላይ ተፅፏል \\n... \n",
"\n",
" created_at possibly_sensitive lang source \\\n",
"0 2021-12-09T21:26:27.000Z False en Twitter Web App \n",
"1 2021-12-09T21:26:06.000Z False en SocialFlow \n",
"2 2021-12-09T21:25:05.000Z False en Twitter Web App \n",
"3 2021-12-09T21:23:26.000Z False en Twitter for iPhone \n",
"4 2021-12-09T21:23:13.000Z False und Twitter for Android \n",
"\n",
" attachments \n",
"0 NaN \n",
"1 {'media_keys': ['3_1469055779160403974']} \n",
"2 NaN \n",
"3 NaN \n",
"4 {'media_keys': ['7_1468804403410857986']} "
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"my_df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8b5641af",
"metadata": {},
"outputs": [],
"source": [
"response_1_dict['meta']"
]
},
{
"cell_type": "markdown",
"id": "e6f069c9",
"metadata": {},
"source": [
"Getting next token"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "70d8dad5",
"metadata": {},
"outputs": [],
"source": [
"my_api_url_2 = my_api_url + '&next_token={}&max_results=100'.format(response_1_dict['meta']['next_token'])"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "7c0c53da",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'https://api.twitter.com/2/tweets/search/recent?query=%23Jeopardy%20OR%20%23KenJennings%20OR%20%40KenJennings%20OR%20%40MissMayim%20OR%20%23MayimBialik%20OR%20%40Jeopardy%20lang%3Aen&tweet.fields=public_metrics,created_at,lang,possibly_sensitive,attachments,source&next_token=b26v89c19zqg8o3fpdy9ckfxcsqie3djesjb40kjan6v1&max_results=100'"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"my_api_url_2"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "eab3e23d",
"metadata": {},
"outputs": [],
"source": [
"response_2 = requests.request(\"GET\", my_api_url_2, headers = header)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "013b721b",
"metadata": {},
"outputs": [],
"source": [
"response_2.text"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "115c46a3",
"metadata": {},
"outputs": [],
"source": [
"my_df = my_df.append(pd.DataFrame(json.loads(response_2.text)['data']), ignore_index= True)"
]
},
{
"cell_type": "markdown",
"id": "431f70f7",
"metadata": {},
"source": [
"Adding the new data collected to existing data frame"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c1fd60ff",
"metadata": {},
"outputs": [],
"source": [
"my_df"
]
},
{
"cell_type": "markdown",
"id": "8deddb5f",
"metadata": {},
"source": [
"My data frame now has 200 records. "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4a329575",
"metadata": {},
"outputs": [],
"source": [
"response_1_dict['meta']"
]
},
{
"cell_type": "markdown",
"id": "2ed09eb1",
"metadata": {},
"source": [
"Collecting next token for next request of tweets. "
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "f2ba32e5",
"metadata": {},
"outputs": [],
"source": [
"response_3 = requests.request(\"GET\", my_api_url_2, headers = header)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "050fce61",
"metadata": {},
"outputs": [],
"source": [
"response_3.text"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "0db7ff29",
"metadata": {},
"outputs": [],
"source": [
"my_df = my_df.append(pd.DataFrame(json.loads(response_3.text)['data']), ignore_index= True)"
]
},
{
"cell_type": "markdown",
"id": "60d146e2",
"metadata": {},
"source": [
"Adding the new tweets I've collected to the other 200 tweets. "
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "970d32b5",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" source \n",
" text \n",
" author_id \n",
" possibly_sensitive \n",
" lang \n",
" id \n",
" public_metrics \n",
" created_at \n",
" attachments \n",
" \n",
" \n",
" \n",
" \n",
" 293 \n",
" Twitter for iPhone \n",
" @equimanthorn_ @Jeopardy Got my vote. \n",
" NaN \n",
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" Twitter for iPhone \n",
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" {'retweet_count': 16, 'reply_count': 0, 'like_... \n",
" 2021-12-09T18:56:44.000Z \n",
" {'media_keys': ['3_1469018140524748801', '3_14... \n",
" \n",
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" Twitter for iPhone \n",
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" NaN \n",
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" {'media_keys': ['3_1469018140524748801', '3_14... \n",
" \n",
" \n",
" 296 \n",
" Twitter for Android \n",
" RT @peterbelayneh: የ #TDF ጀግንነት ታሪክ ላይ ተፅፏል \\n... \n",
" NaN \n",
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" 1469017354508161024 \n",
" {'retweet_count': 268, 'reply_count': 0, 'like... \n",
" 2021-12-09T18:53:24.000Z \n",
" {'media_keys': ['7_1468804403410857986']} \n",
" \n",
" \n",
" 297 \n",
" Twitter for iPhone \n",
" @buzztronics @Jeopardy @missmayim @KenJennings... \n",
" NaN \n",
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" \n",
"
\n",
"
"
],
"text/plain": [
" source text \\\n",
"293 Twitter for iPhone @equimanthorn_ @Jeopardy Got my vote. \n",
"294 Twitter for iPhone RT @missmayim: Class is in session! Welcome to... \n",
"295 Twitter for iPhone Class is in session! Welcome to the first-ever... \n",
"296 Twitter for Android RT @peterbelayneh: የ #TDF ጀግንነት ታሪክ ላይ ተፅፏል \\n... \n",
"297 Twitter for iPhone @buzztronics @Jeopardy @missmayim @KenJennings... \n",
"\n",
" author_id possibly_sensitive lang id \\\n",
"293 NaN False en 1469018445693943812 \n",
"294 NaN False en 1469018191850573833 \n",
"295 NaN False en 1469018145268453378 \n",
"296 NaN False und 1469017354508161024 \n",
"297 NaN False en 1469017228385345536 \n",
"\n",
" public_metrics \\\n",
"293 {'retweet_count': 0, 'reply_count': 0, 'like_c... \n",
"294 {'retweet_count': 16, 'reply_count': 0, 'like_... \n",
"295 {'retweet_count': 16, 'reply_count': 12, 'like... \n",
"296 {'retweet_count': 268, 'reply_count': 0, 'like... \n",
"297 {'retweet_count': 0, 'reply_count': 0, 'like_c... \n",
"\n",
" created_at \\\n",
"293 2021-12-09T18:57:45.000Z \n",
"294 2021-12-09T18:56:44.000Z \n",
"295 2021-12-09T18:56:33.000Z \n",
"296 2021-12-09T18:53:24.000Z \n",
"297 2021-12-09T18:52:54.000Z \n",
"\n",
" attachments \n",
"293 NaN \n",
"294 {'media_keys': ['3_1469018140524748801', '3_14... \n",
"295 {'media_keys': ['3_1469018140524748801', '3_14... \n",
"296 {'media_keys': ['7_1468804403410857986']} \n",
"297 NaN "
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"my_df.tail()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "43fbd59e",
"metadata": {},
"outputs": [],
"source": [
"response_1_dict['meta']"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "08c43944",
"metadata": {},
"outputs": [],
"source": [
"response_4 = requests.request(\"GET\", my_api_url_2, headers = header)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5245d895",
"metadata": {},
"outputs": [],
"source": [
"response_4.text"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "96b5daed",
"metadata": {},
"outputs": [],
"source": [
"my_df = my_df.append(pd.DataFrame(json.loads(response_4.text)['data']), ignore_index= True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2e5fb4b8",
"metadata": {},
"outputs": [],
"source": [
"my_df"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d4c60e0b",
"metadata": {},
"outputs": [],
"source": [
"response_1_dict['meta']"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "592551cb",
"metadata": {},
"outputs": [],
"source": [
"response_5 = requests.request(\"GET\", my_api_url_2, headers = header)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "44c9243d",
"metadata": {},
"outputs": [],
"source": [
"response_5.text"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "afc51057",
"metadata": {},
"outputs": [],
"source": [
"my_df = my_df.append(pd.DataFrame(json.loads(response_4.text)['data']), ignore_index= True)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "0471035f",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" public_metrics \n",
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" {'media_keys': ['3_1468330655360507904']} \n",
" \n",
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" {'retweet_count': 16, 'reply_count': 0, 'like_... \n",
" 1469018736174764033 \n",
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" False \n",
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" Twitter for iPhone \n",
" {'media_keys': ['3_1469018140524748801', '3_14... \n",
" \n",
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" \n",
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" Twitter for Android \n",
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" \n",
" \n",
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"
"
],
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" public_metrics id \\\n",
"395 {'retweet_count': 60, 'reply_count': 0, 'like_... 1469018769599221767 \n",
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"397 {'retweet_count': 274, 'reply_count': 0, 'like... 1469018735776354316 \n",
"398 {'retweet_count': 20, 'reply_count': 0, 'like_... 1469018669334290434 \n",
"399 {'retweet_count': 16, 'reply_count': 0, 'like_... 1469018642138349572 \n",
"\n",
" author_id text \\\n",
"395 NaN RT @Jeopardy: Thank you to educators everywher... \n",
"396 NaN RT @missmayim: Class is in session! Welcome to... \n",
"397 NaN RT @Jeopardy: We are delighted to let you know... \n",
"398 NaN RT @missmayim: Guess what! Today is the day?! ... \n",
"399 NaN RT @missmayim: Class is in session! Welcome to... \n",
"\n",
" created_at possibly_sensitive lang source \\\n",
"395 2021-12-09T18:59:02.000Z False en Twitter for Android \n",
"396 2021-12-09T18:58:54.000Z False en Twitter for iPhone \n",
"397 2021-12-09T18:58:54.000Z False en Twitter for Android \n",
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"\n",
" attachments \n",
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"397 NaN \n",
"398 NaN \n",
"399 {'media_keys': ['3_1469018140524748801', '3_14... "
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"my_df.tail()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1e82bb74",
"metadata": {},
"outputs": [],
"source": [
"response_1_dict['meta']"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "4291fc70",
"metadata": {},
"outputs": [],
"source": [
"response_6 = requests.request(\"GET\", my_api_url_2, headers = header)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "6453591b",
"metadata": {},
"outputs": [],
"source": [
"my_df = my_df.append(pd.DataFrame(json.loads(response_4.text)['data']), ignore_index= True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "36648cd4",
"metadata": {},
"outputs": [],
"source": [
"my_df"
]
},
{
"cell_type": "markdown",
"id": "f930b7d0",
"metadata": {},
"source": [
"My data frame now has 500 tweets. This is the amount of tweets I would like to collect about Jeopardy!, Ken Jennings, or Mayim Bialik (Jeopardy! hosts)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "33624d49",
"metadata": {},
"outputs": [],
"source": [
"my_df.to_csv('jeopardy_data.csv')"
]
},
{
"cell_type": "markdown",
"id": "5904a703",
"metadata": {},
"source": [
"Converting data to .csv file for further analysis"
]
},
{
"cell_type": "markdown",
"id": "b427acae",
"metadata": {},
"source": [
"## WOF Tweet Gathering"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0a4864cd",
"metadata": {},
"outputs": [],
"source": [
"response_1_dict['meta']"
]
},
{
"cell_type": "markdown",
"id": "ed74b94b",
"metadata": {},
"source": [
"This gives me the next_token I can use to make another request."
]
},
{
"cell_type": "code",
"execution_count": 67,
"id": "156107f0",
"metadata": {},
"outputs": [],
"source": [
"endpoint_url_3 = 'https://api.twitter.com/2/tweets/search/recent'"
]
},
{
"cell_type": "markdown",
"id": "02a106b8",
"metadata": {},
"source": [
"Here I am creating a new url to pull tweets from the Twitter API"
]
},
{
"cell_type": "code",
"execution_count": 68,
"id": "b7c0b910",
"metadata": {},
"outputs": [],
"source": [
"query = urllib.parse.quote('#WOF OR #WheelOfFourtune OR #PatSajak OR @PatSajak lang:en')"
]
},
{
"cell_type": "markdown",
"id": "90c03fe3",
"metadata": {},
"source": [
"Here I am creating a second query to search for Wheel of Fortune or Pat Sajak"
]
},
{
"cell_type": "code",
"execution_count": 69,
"id": "9a33c981",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'%23WOF%20OR%20%23WheelOfFourtune%20OR%20%23PatSajak%20OR%20%40PatSajak%20lang%3Aen'"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query"
]
},
{
"cell_type": "code",
"execution_count": 70,
"id": "fec8400d",
"metadata": {},
"outputs": [],
"source": [
"tweet_fields = 'public_metrics,created_at,lang,possibly_sensitive,attachments,source'"
]
},
{
"cell_type": "markdown",
"id": "bdc2b5c9",
"metadata": {},
"source": [
"Here I am defining the tweet fields I would like the API to include when I ask for data."
]
},
{
"cell_type": "code",
"execution_count": 71,
"id": "c2b79736",
"metadata": {},
"outputs": [],
"source": [
"expansions = 'author_id'"
]
},
{
"cell_type": "code",
"execution_count": 72,
"id": "d6158afd",
"metadata": {},
"outputs": [],
"source": [
"url = endpoint_url_3 + '?query={}&tweet.fields={}'.format(query, tweet_fields)"
]
},
{
"cell_type": "code",
"execution_count": 73,
"id": "09d9bb59",
"metadata": {},
"outputs": [],
"source": [
"url_expansions = endpoint_url_3 + '?query={}&max_results=100&tweet.fields={}&expansions={}&user.fields={}'.format(query, tweet_fields, expansions, 'username')"
]
},
{
"cell_type": "markdown",
"id": "3b0bbcd8",
"metadata": {},
"source": [
"Here I am specifying the fields and conditions that I would like to see from the Twitter data collected. "
]
},
{
"cell_type": "code",
"execution_count": 74,
"id": "ada3f337",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'https://api.twitter.com/2/tweets/search/recent?query=%23WOF%20OR%20%23WheelOfFourtune%20OR%20%23PatSajak%20OR%20%40PatSajak%20lang%3Aen&max_results=100&tweet.fields=public_metrics,created_at,lang,possibly_sensitive,attachments,source&expansions=author_id&user.fields=username'"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"url_expansions"
]
},
{
"cell_type": "markdown",
"id": "e27b36fb",
"metadata": {},
"source": [
"This is the url of my request from Twitter of tweets regarding WOF and Pat Sajak"
]
},
{
"cell_type": "code",
"execution_count": 75,
"id": "f23dffbf",
"metadata": {},
"outputs": [],
"source": [
"Wresponse_1 = requests.request(\"GET\", url_expansions, headers = header)"
]
},
{
"cell_type": "markdown",
"id": "23b50e08",
"metadata": {},
"source": [
"Here I am making my request for 100 tweets of Twitter data. This time about WOF"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3f9d6a8b",
"metadata": {},
"outputs": [],
"source": [
"Wresponse_1.text"
]
},
{
"cell_type": "code",
"execution_count": 76,
"id": "b0f6e777",
"metadata": {},
"outputs": [],
"source": [
"Wresponse_1_dict = json.loads(Wresponse_1.text)"
]
},
{
"cell_type": "code",
"execution_count": 77,
"id": "827c2bb2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys(['data', 'includes', 'meta'])"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Wresponse_1_dict.keys()"
]
},
{
"cell_type": "code",
"execution_count": 78,
"id": "f6cf4b58",
"metadata": {},
"outputs": [],
"source": [
"my_df2 = pd.DataFrame(Wresponse_1_dict['data'])"
]
},
{
"cell_type": "markdown",
"id": "ce463da6",
"metadata": {},
"source": [
"I am creating a data frame with the 'data' key."
]
},
{
"cell_type": "code",
"execution_count": 79,
"id": "eaf728a3",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" possibly_sensitive \n",
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" lang \n",
" source \n",
" public_metrics \n",
" created_at \n",
" id \n",
" attachments \n",
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" \n",
" 95 \n",
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" 98 \n",
" False \n",
" @MaryMacMixes @patsajak Wellcare \n",
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" Twitter for iPhone \n",
" {'retweet_count': 107, 'reply_count': 0, 'like... \n",
" 2021-12-09T18:16:24.000Z \n",
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"
"
],
"text/plain": [
" possibly_sensitive text \\\n",
"95 False @sandergansen @WorldOfFreight @supplain Im so ... \n",
"96 False RT @patsajak: “Please hold. Your call is impor... \n",
"97 False RT @patsajak: “Please hold. Your call is impor... \n",
"98 False @MaryMacMixes @patsajak Wellcare \n",
"99 False RT @patsajak: “Please hold. Your call is impor... \n",
"\n",
" author_id lang source \\\n",
"95 261232333 en Twitter Web App \n",
"96 505353338 en Twitter for Android \n",
"97 297558558 en Twitter for iPhone \n",
"98 1007792130 en Twitter for iPad \n",
"99 1402776664866701317 en Twitter for iPhone \n",
"\n",
" public_metrics \\\n",
"95 {'retweet_count': 5, 'reply_count': 3, 'like_c... \n",
"96 {'retweet_count': 107, 'reply_count': 0, 'like... \n",
"97 {'retweet_count': 107, 'reply_count': 0, 'like... \n",
"98 {'retweet_count': 0, 'reply_count': 0, 'like_c... \n",
"99 {'retweet_count': 107, 'reply_count': 0, 'like... \n",
"\n",
" created_at id attachments \n",
"95 2021-12-09T18:23:03.000Z 1469009714826653702 NaN \n",
"96 2021-12-09T18:19:58.000Z 1469008938909806593 NaN \n",
"97 2021-12-09T18:18:47.000Z 1469008641047019526 NaN \n",
"98 2021-12-09T18:17:50.000Z 1469008403594887169 NaN \n",
"99 2021-12-09T18:16:24.000Z 1469008040959655940 NaN "
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"my_df2.tail()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7592aa18",
"metadata": {},
"outputs": [],
"source": [
"Wresponse_1_dict['meta']"
]
},
{
"cell_type": "code",
"execution_count": 82,
"id": "ebc3064e",
"metadata": {},
"outputs": [],
"source": [
"my_api_W_url = url + '&next_token={}&max_results=100'.format(Wresponse_1_dict['meta']['next_token'])"
]
},
{
"cell_type": "code",
"execution_count": 83,
"id": "26732e25",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'https://api.twitter.com/2/tweets/search/recent?query=%23WOF%20OR%20%23WheelOfFourtune%20OR%20%23PatSajak%20OR%20%40PatSajak%20lang%3Aen&tweet.fields=public_metrics,created_at,lang,possibly_sensitive,attachments,source&next_token=b26v89c19zqg8o3fpdy9ckf1a4r3yi0250sn60qqfu5x9&max_results=100'"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"my_api_W_url"
]
},
{
"cell_type": "code",
"execution_count": 84,
"id": "5ea024df",
"metadata": {},
"outputs": [],
"source": [
"response_W2 = requests.request(\"GET\", my_api_W_url, headers = header)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "409533c8",
"metadata": {},
"outputs": [],
"source": [
"response_W2.text"
]
},
{
"cell_type": "code",
"execution_count": 86,
"id": "f6f6a8af",
"metadata": {},
"outputs": [],
"source": [
"my_df2 = my_df2.append(pd.DataFrame(json.loads(response_W2.text)['data']), ignore_index= True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "90b518b5",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"my_df2"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7cd66f43",
"metadata": {},
"outputs": [],
"source": [
"Wresponse_1_dict['meta']"
]
},
{
"cell_type": "code",
"execution_count": 89,
"id": "00fced89",
"metadata": {},
"outputs": [],
"source": [
"response_W3 = requests.request(\"GET\", my_api_W_url, headers = header)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0c02a3b6",
"metadata": {},
"outputs": [],
"source": [
"response_W3.text"
]
},
{
"cell_type": "code",
"execution_count": 91,
"id": "d7f942bd",
"metadata": {},
"outputs": [],
"source": [
"my_df2 = my_df2.append(pd.DataFrame(json.loads(response_W3.text)['data']), ignore_index= True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "539c4d94",
"metadata": {},
"outputs": [],
"source": [
"my_df2"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "330d5bfb",
"metadata": {},
"outputs": [],
"source": [
"Wresponse_1_dict['meta']"
]
},
{
"cell_type": "code",
"execution_count": 94,
"id": "cc7cfbc8",
"metadata": {},
"outputs": [],
"source": [
"response_W4 = requests.request(\"GET\", my_api_W_url, headers = header)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f8b82763",
"metadata": {},
"outputs": [],
"source": [
"response_W4.text"
]
},
{
"cell_type": "code",
"execution_count": 96,
"id": "34bd1e4c",
"metadata": {},
"outputs": [],
"source": [
"my_df2 = my_df2.append(pd.DataFrame(json.loads(response_W4.text)['data']), ignore_index= True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bcdac4fa",
"metadata": {},
"outputs": [],
"source": [
"my_df2"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "074d5d78",
"metadata": {},
"outputs": [],
"source": [
"Wresponse_1_dict['meta']"
]
},
{
"cell_type": "code",
"execution_count": 99,
"id": "c46362ff",
"metadata": {},
"outputs": [],
"source": [
"response_W5 = requests.request(\"GET\", my_api_W_url, headers = header)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b1a280cc",
"metadata": {},
"outputs": [],
"source": [
"response_W5.text"
]
},
{
"cell_type": "code",
"execution_count": 101,
"id": "7782052d",
"metadata": {},
"outputs": [],
"source": [
"my_df2 = my_df2.append(pd.DataFrame(json.loads(response_W5.text)['data']), ignore_index= True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4d216181",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 105,
"id": "87d32743",
"metadata": {},
"outputs": [],
"source": [
"my_df2.to_csv('WOF_data.csv')"
]
},
{
"cell_type": "markdown",
"id": "74e81525",
"metadata": {},
"source": [
"Moving my Wheel of Fortune into a csv. "
]
},
{
"cell_type": "markdown",
"id": "fac5607a",
"metadata": {},
"source": [
"# Analysis"
]
},
{
"cell_type": "markdown",
"id": "24c5dbd2",
"metadata": {},
"source": [
"Here I will be showing the analysis of Jeopardy! and WOF tweets and comparing them. \n",
"\n",
"From here, I used oly to sort the tweets with only english tweets. Then I used polarity analyzer to get the calculations about positive, neutral, and negative tweets. I will interpret the results of the tweets positively and negatively with the polarity analyzer. Lastly, I used matplotlib to visualize my data.\n",
"\n",
"Weaknesses of my analysis: \n",
"- It is a little broad\n",
"- Jeopardy! and WOF's main audience might not be on Twitter maybe a different social media platform\n",
" - meaning I might only receive data from a younger or older audience\n",
"\n",
"Strengths of my analysis:\n",
"- A new tournament has begun which means possibly an influx of tweets\n"
]
},
{
"cell_type": "markdown",
"id": "6433c812",
"metadata": {},
"source": [
"Beginning to analyze data I've collected in each csv. "
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "84ca61df",
"metadata": {},
"outputs": [],
"source": [
"oly = pd.read_csv('jeopardy_data.csv', encoding = 'utf-8')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "950a7601",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Unnamed: 0', 'public_metrics', 'id', 'author_id', 'text', 'created_at',\n",
" 'possibly_sensitive', 'lang', 'source', 'attachments'],\n",
" dtype='object')"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"oly.keys()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "804c44da",
"metadata": {},
"outputs": [
{
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},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"oly.head()"
]
},
{
"cell_type": "markdown",
"id": "cbfaab21",
"metadata": {},
"source": [
"Determining the keys in my CSV"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "7670fa66",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 @JenniferBurt @TheDudeInSF @missmayim You are ...\n",
"1 #Jeopardy! fans are obsessed with this contest...\n",
"2 @Jeopardy Does anyone else think he looks a lo...\n",
"3 RT @KenJennings: Delighted that @missmayim and...\n",
"4 RT @peterbelayneh: የ #TDF ጀግንነት ታሪክ ላይ ተፅፏል \\n...\n",
" ... \n",
"495 RT @Jeopardy: Thank you to educators everywher...\n",
"496 RT @missmayim: Class is in session! Welcome to...\n",
"497 RT @Jeopardy: We are delighted to let you know...\n",
"498 RT @missmayim: Guess what! Today is the day?! ...\n",
"499 RT @missmayim: Class is in session! Welcome to...\n",
"Name: text, Length: 500, dtype: object"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"oly['text']"
]
},
{
"cell_type": "markdown",
"id": "5287cf02",
"metadata": {},
"source": [
"Generating the raw text of the tweets so I can evaluate the positive, neutral, and negative words. "
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "033a57d0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'RT @peterbelayneh: የ #TDF ጀግንነት ታሪክ ላይ ተፅፏል \\nThe most-watched show #Jeopardy https://t.co/dNlxV1kLhu'"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"oly['text'].iloc[6]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "141c071b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 en\n",
"1 en\n",
"2 en\n",
"3 en\n",
"4 und\n",
" ... \n",
"495 en\n",
"496 en\n",
"497 en\n",
"498 en\n",
"499 en\n",
"Name: lang, Length: 500, dtype: category\n",
"Categories (4, object): ['ca', 'en', 'ro', 'und']"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"oly['lang'].astype('category')"
]
},
{
"cell_type": "markdown",
"id": "66aad707",
"metadata": {},
"source": [
"Pulling only english tweets"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "2c5bf14e",
"metadata": {},
"outputs": [],
"source": [
"oly_enposts = oly[(oly['lang'] == 'en')]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "82ef41ab",
"metadata": {},
"outputs": [
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".. ... \n",
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"497 RT @Jeopardy: We are delighted to let you know... \n",
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"499 RT @missmayim: Class is in session! Welcome to... \n",
"\n",
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".. ... ... ... ... \n",
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},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"oly_enposts"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "d5581952",
"metadata": {},
"outputs": [],
"source": [
"analyser = SentimentIntensityAnalyzer()"
]
},
{
"cell_type": "markdown",
"id": "07fb9cb8",
"metadata": {},
"source": [
"Introducing the sentiment intensity analyzer which will help us to evaluate the words in the posts. "
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "7f8485a9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'neg': 0.157, 'neu': 0.66, 'pos': 0.182, 'compound': 0.126}\n"
]
}
],
"source": [
"print(analyser.polarity_scores(oly_enposts['text'].iloc[3]))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "6d94b8b7",
"metadata": {},
"outputs": [],
"source": [
"a_sent = analyser.polarity_scores(oly_enposts['text'].iloc[3])"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "11384293",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.182"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a_sent['pos']"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "ab4ee43d",
"metadata": {},
"outputs": [],
"source": [
"sentiments = [analyser.polarity_scores(x) for x in oly_enposts['text']]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "d24d49ff",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0},\n",
" {'neg': 0.281, 'neu': 0.719, 'pos': 0.0, 'compound': -0.6239},\n",
" {'neg': 0.204, 'neu': 0.649, 'pos': 0.147, 'compound': -0.2406},\n",
" {'neg': 0.157, 'neu': 0.66, 'pos': 0.182, 'compound': 0.126},\n",
" {'neg': 0.146, 'neu': 0.66, 'pos': 0.193, 'compound': 0.25},\n",
" {'neg': 0.058, 'neu': 0.765, 'pos': 0.177, 'compound': 0.7494},\n",
" {'neg': 0.227, 'neu': 0.773, 'pos': 0.0, 'compound': -0.5267},\n",
" {'neg': 0.255, 'neu': 0.745, 'pos': 0.0, 'compound': -0.7574},\n",
" {'neg': 0.0, 'neu': 0.867, 'pos': 0.133, 'compound': 0.3182},\n",
" {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sentiments[0:10]"
]
},
{
"cell_type": "markdown",
"id": "791cfa58",
"metadata": {},
"source": [
"Showing the sentiment scores of the first 10 tweets. "
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "220df492",
"metadata": {},
"outputs": [],
"source": [
"sentiments_df = pd.DataFrame(sentiments)"
]
},
{
"cell_type": "markdown",
"id": "a7272e9f",
"metadata": {},
"source": [
"Putting sentiments collected into a data frame for better visualization. "
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "eb1ee5ff",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" neg \n",
" neu \n",
" pos \n",
" compound \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 0.000 \n",
" 1.000 \n",
" 0.000 \n",
" 0.0000 \n",
" \n",
" \n",
" 1 \n",
" 0.281 \n",
" 0.719 \n",
" 0.000 \n",
" -0.6239 \n",
" \n",
" \n",
" 2 \n",
" 0.204 \n",
" 0.649 \n",
" 0.147 \n",
" -0.2406 \n",
" \n",
" \n",
" 3 \n",
" 0.157 \n",
" 0.660 \n",
" 0.182 \n",
" 0.1260 \n",
" \n",
" \n",
" 4 \n",
" 0.146 \n",
" 0.660 \n",
" 0.193 \n",
" 0.2500 \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" neg neu pos compound\n",
"0 0.000 1.000 0.000 0.0000\n",
"1 0.281 0.719 0.000 -0.6239\n",
"2 0.204 0.649 0.147 -0.2406\n",
"3 0.157 0.660 0.182 0.1260\n",
"4 0.146 0.660 0.193 0.2500"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sentiments_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "65ab10f3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 419.000000\n",
"mean 0.127823\n",
"std 0.128450\n",
"min 0.000000\n",
"25% 0.000000\n",
"50% 0.133000\n",
"75% 0.165500\n",
"max 0.571000\n",
"Name: pos, dtype: float64"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sentiments_df['pos'].describe()"
]
},
{
"cell_type": "markdown",
"id": "f935cb7d",
"metadata": {},
"source": [
"SHowing stats of the tweets collected"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "73916005",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 419.000000\n",
"mean 0.154847\n",
"std 0.141418\n",
"min 0.000000\n",
"25% 0.000000\n",
"50% 0.161000\n",
"75% 0.212000\n",
"max 0.756000\n",
"Name: neg, dtype: float64"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sentiments_df['neg'].describe()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "85febffa",
"metadata": {},
"outputs": [],
"source": [
"from matplotlib import pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "2f5db6bf",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig0, ax0 = plt.subplots()\n",
"ax0.hist(sentiments_df['pos'])\n",
"ax0.set_title(\"Jeopardy Tweet Scores\")\n",
"ax0.set_xlabel(\"Positivity Amount\") \n",
"ax0.set_ylabel(\"Frequencies\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "d84697dc",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig1, ax1 = plt.subplots()\n",
"ax1.hist(sentiments_df['neg'])\n",
"ax1.set_title(\"Jeopardy Tweet Scores\") \n",
"ax1.set_xlabel(\"Negativity Amount\") \n",
"ax1.set_ylabel(\"Frequencies\") \n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "4ccfe5ed",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig3, ax3 = plt.subplots()\n",
"ax3.hist(sentiments_df['neu'])\n",
"ax3.set_title(\"Jeopardy Tweet Scores\") \n",
"ax3.set_xlabel(\"Neutral Amount\") \n",
"ax3.set_ylabel(\"Frequencies\") \n",
"plt.show()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "f8152d9a",
"metadata": {},
"outputs": [],
"source": [
"oly = pd.read_csv('WOF_data.csv', encoding = 'utf-8')"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "74c49b39",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Unnamed: 0', 'possibly_sensitive', 'text', 'author_id', 'lang',\n",
" 'source', 'public_metrics', 'created_at', 'id', 'attachments'],\n",
" dtype='object')"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"oly.keys()"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "2a122985",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
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" \n",
" \n",
" \n",
" Unnamed: 0 \n",
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" text \n",
" author_id \n",
" lang \n",
" source \n",
" public_metrics \n",
" created_at \n",
" id \n",
" attachments \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
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" False \n",
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" 7.406850e+17 \n",
" en \n",
" Twitter for iPhone \n",
" {'retweet_count': 1014, 'reply_count': 0, 'lik... \n",
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" 2.752687e+08 \n",
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" {'retweet_count': 1014, 'reply_count': 0, 'lik... \n",
" 2021-12-09T21:17:37.000Z \n",
" 1469053646876905475 \n",
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" 2 \n",
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" 1.409894e+18 \n",
" en \n",
" Twitter for Android \n",
" {'retweet_count': 1014, 'reply_count': 0, 'lik... \n",
" 2021-12-09T21:17:29.000Z \n",
" 1469053612580028422 \n",
" NaN \n",
" \n",
" \n",
" 3 \n",
" 3 \n",
" False \n",
" RT @patsajak: I don’t know which is stranger: ... \n",
" 1.820274e+08 \n",
" en \n",
" Twitter for Android \n",
" {'retweet_count': 281, 'reply_count': 0, 'like... \n",
" 2021-12-09T21:13:40.000Z \n",
" 1469052653019832322 \n",
" NaN \n",
" \n",
" \n",
" 4 \n",
" 4 \n",
" False \n",
" @patsajak @proudtigerlsu Start by encouraging ... \n",
" 1.330241e+18 \n",
" en \n",
" Twitter Web App \n",
" {'retweet_count': 0, 'reply_count': 0, 'like_c... \n",
" 2021-12-09T21:11:57.000Z \n",
" 1469052218737414150 \n",
" NaN \n",
" \n",
" \n",
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\n",
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"
],
"text/plain": [
" Unnamed: 0 possibly_sensitive \\\n",
"0 0 False \n",
"1 1 False \n",
"2 2 False \n",
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"4 4 False \n",
"\n",
" text author_id lang \\\n",
"0 RT @patsajak: A Christmas message to my fellow... 7.406850e+17 en \n",
"1 RT @patsajak: A Christmas message to my fellow... 2.752687e+08 en \n",
"2 RT @patsajak: A Christmas message to my fellow... 1.409894e+18 en \n",
"3 RT @patsajak: I don’t know which is stranger: ... 1.820274e+08 en \n",
"4 @patsajak @proudtigerlsu Start by encouraging ... 1.330241e+18 en \n",
"\n",
" source public_metrics \\\n",
"0 Twitter for iPhone {'retweet_count': 1014, 'reply_count': 0, 'lik... \n",
"1 Twitter for iPhone {'retweet_count': 1014, 'reply_count': 0, 'lik... \n",
"2 Twitter for Android {'retweet_count': 1014, 'reply_count': 0, 'lik... \n",
"3 Twitter for Android {'retweet_count': 281, 'reply_count': 0, 'like... \n",
"4 Twitter Web App {'retweet_count': 0, 'reply_count': 0, 'like_c... \n",
"\n",
" created_at id attachments \n",
"0 2021-12-09T21:19:18.000Z 1469054067867533317 NaN \n",
"1 2021-12-09T21:17:37.000Z 1469053646876905475 NaN \n",
"2 2021-12-09T21:17:29.000Z 1469053612580028422 NaN \n",
"3 2021-12-09T21:13:40.000Z 1469052653019832322 NaN \n",
"4 2021-12-09T21:11:57.000Z 1469052218737414150 NaN "
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"oly.head()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "89147f77",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 en\n",
"1 en\n",
"2 en\n",
"3 en\n",
"4 en\n",
" ..\n",
"495 en\n",
"496 en\n",
"497 en\n",
"498 en\n",
"499 en\n",
"Name: lang, Length: 500, dtype: category\n",
"Categories (3, object): ['en', 'tr', 'und']"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"oly['lang'].astype('category')"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "119bdcee",
"metadata": {},
"outputs": [],
"source": [
"oly_enposts = oly[(oly['lang'] == 'en')]"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "869646d4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 RT @patsajak: A Christmas message to my fellow...\n",
"1 RT @patsajak: A Christmas message to my fellow...\n",
"2 RT @patsajak: A Christmas message to my fellow...\n",
"3 RT @patsajak: I don’t know which is stranger: ...\n",
"4 @patsajak @proudtigerlsu Start by encouraging ...\n",
"Name: text, dtype: object"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"oly_enposts['text'].head()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "6f2eec92",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'RT @patsajak: “Please hold. Your call is important to us. Just not important enough to hire enough operators to answer your call.”'"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"oly_enposts['text'].iloc[6]"
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "6c212d83",
"metadata": {},
"outputs": [],
"source": [
"analyser = SentimentIntensityAnalyzer()"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "3561e7a8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'neg': 0.0, 'neu': 0.913, 'pos': 0.087, 'compound': 0.2716}\n"
]
}
],
"source": [
"print(analyser.polarity_scores(oly_enposts['text'].iloc[3]))"
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "0862e34a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'RT @patsajak: A Christmas message to my fellow Californians: If you plan to steal merchandise, please keep it under $950. This way, it’s a…'"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"oly_enposts['text'].iloc[400]"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "0615f4ab",
"metadata": {},
"outputs": [],
"source": [
"a_sent = analyser.polarity_scores(oly_enposts['text'].iloc[400])"
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "eabf1834",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.084"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a_sent['pos']"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "fb2fd1ac",
"metadata": {},
"outputs": [],
"source": [
"sentiments = [analyser.polarity_scores(x) for x in oly_enposts['text']]"
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "f899c4eb",
"metadata": {},
"outputs": [],
"source": [
"sentiments_df = pd.DataFrame(sentiments)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "edbd39f6",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" neg \n",
" neu \n",
" pos \n",
" compound \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 0.116 \n",
" 0.800 \n",
" 0.084 \n",
" -0.2263 \n",
" \n",
" \n",
" 1 \n",
" 0.116 \n",
" 0.800 \n",
" 0.084 \n",
" -0.2263 \n",
" \n",
" \n",
" 2 \n",
" 0.116 \n",
" 0.800 \n",
" 0.084 \n",
" -0.2263 \n",
" \n",
" \n",
" 3 \n",
" 0.000 \n",
" 0.913 \n",
" 0.087 \n",
" 0.2716 \n",
" \n",
" \n",
" 4 \n",
" 0.109 \n",
" 0.741 \n",
" 0.150 \n",
" 0.2960 \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" neg neu pos compound\n",
"0 0.116 0.800 0.084 -0.2263\n",
"1 0.116 0.800 0.084 -0.2263\n",
"2 0.116 0.800 0.084 -0.2263\n",
"3 0.000 0.913 0.087 0.2716\n",
"4 0.109 0.741 0.150 0.2960"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sentiments_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "898cedb4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 498.000000\n",
"mean 0.096492\n",
"std 0.091340\n",
"min 0.000000\n",
"25% 0.077000\n",
"50% 0.077000\n",
"75% 0.084000\n",
"max 0.610000\n",
"Name: pos, dtype: float64"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sentiments_df['pos'].describe()"
]
},
{
"cell_type": "code",
"execution_count": 58,
"id": "bc80888f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 498.000000\n",
"mean 0.066952\n",
"std 0.052092\n",
"min 0.000000\n",
"25% 0.000000\n",
"50% 0.068000\n",
"75% 0.116000\n",
"max 0.545000\n",
"Name: neg, dtype: float64"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sentiments_df['neg'].describe()"
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "f5f816f8",
"metadata": {},
"outputs": [],
"source": [
"from matplotlib import pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 61,
"id": "492f06e6",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig0, ax0 = plt.subplots()\n",
"ax0.hist(sentiments_df['pos'])\n",
"ax0.set_title(\"Wheel of Fourtune Tweet Scores\") \n",
"ax0.set_xlabel(\"Positivity Amount\") \n",
"ax0.set_ylabel(\"Frequencies\") \n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "0a8700ae",
"metadata": {},
"source": [
"Graph based on the positive sentiment % of the tweets regarding WOF. "
]
},
{
"cell_type": "code",
"execution_count": 62,
"id": "b4b6ad2d",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig1, ax1 = plt.subplots()\n",
"ax1.hist(sentiments_df['neg'])\n",
"ax1.set_title(\"Wheel of Fourtune Tweet Scores\") \n",
"ax1.set_xlabel(\"Negativity Amount\") \n",
"ax1.set_ylabel(\"Frequencies\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 63,
"id": "2ea37f4e",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig3, ax3 = plt.subplots()\n",
"ax3.hist(sentiments_df['neu'])\n",
"ax3.set_title(\"Wheel of Fourtune Tweet Scores\") \n",
"ax3.set_xlabel(\"Neutral Amount\") \n",
"ax3.set_ylabel(\"Frequencies\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "6b81c631",
"metadata": {},
"source": [
"### Analysis:"
]
},
{
"cell_type": "markdown",
"id": "87fa6304",
"metadata": {},
"source": [
"The data is organized within data frames. Once I had all 500 tweets about each subject, they are sent to a csv/excel file for easier analysis. \n",
"\n",
"Limitations\n",
"- Some limitations I've had were attempting to steer clear of a null hypothesis, and ensuring a univariant distribution. \n",
"\n",
"Conclusion:\n",
"- Comparing the positive and negative scores, Jeopardy! had a higher positive mean value of 0.127. WOF had a 0.096 positive mean score. But, Jeopardy! had a much higher negative mean value than WOF at 0.154 while WOF had 0.066. \n",
"- My conclusion is, there are simply more people on Twitter talking about Jeopardy!. Some people might not tweet until they have felt a certain emotion after watching. I think that Jeopardy! could be the more watched show because there are more tweets regarding it, Or the fanbase of Jeopardy! could use Twitter more than fans of WOF. \n",
"- Another possibility is, Jeopardy! could me less liked. This is because while there are more unique tweets regarding Jeopardy!, they are more negative 0.27. \n",
"- There are multiple ways that I could expand this analysis. For example, I could expand on this analysis by using other social media platforms to get a better sample of data. "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.6"
}
},
"nbformat": 4,
"nbformat_minor": 5
}