{ "metadata": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.3-final" }, "orig_nbformat": 2, "kernelspec": { "name": "Python 3.8.3 64-bit", "display_name": "Python 3.8.3 64-bit", "metadata": { "interpreter": { "hash": "0de36b31320ba4c88b4f85a74724f3d16c36a44df48581253710b1065e752d9e" } } } }, "nbformat": 4, "nbformat_minor": 2, "cells": [ { "source": [ "" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# Donald Trumps Nickname Habits\n", "\n", "Hello! In this notebook I will be doing a going through data that I pulled from wikipedia. The data shows the unique nickname given by Donald Trump to various people, groups, and organizations!\n", "\n", "+ Which can be found here: https://en.wikipedia.org/wiki/List_of_nicknames_used_by_Donald_Trump\n", "\n", "Due to the very short nature of the data, I just copied and pasted it directly from the webpage. However, this means that the data will be very messy and needs to be cleaned. In this notebook I will be doing the following:\n", "\n", "+ cleaning the data\n", "+ performing a simple EDA\n", " - what are the nicknaming habits of Trump?\n", "+ assessing and prepping the data for NLP and a Neural Network\n", "\n", "The overarching goal is to create a simple web application using Pheonix and Elixir, and to use a neural framework (TensorFlow, Keras, Torch) to train a model that will generate a Trump Nickname based on the input of a name by the user.\n", "\n", "Lets dive into the data!\n", "\n", "# What does the data look like?" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " fake real \\\n", "0 Dumbo[5] Randolph Tex Alles \n", "1 Where's Hunter[6] Hunter Biden \n", "2 1% Joe[7] / Basement Joe[8] / Beijing Joe[9] /... Joe Biden \n", "3 Little Michael Bloomberg[19] / Mini Mike[20] /... Michael Bloomberg \n", "4 Da Nang Richard[22] / Da Nang Dick[23] Richard Blumenthal \n", "\n", " notes \\\n", "0 Director of the United States Secret Service \n", "1 American lawyer and lobbyist who is the second... \n", "2 47th vice president of the United States; form... \n", "3 108th Mayor of New York City; 2020 Democratic ... \n", "4 U.S. senator from Connecticut; 23rd attorney g... \n", "\n", " category \n", "0 domestic political figures \n", "1 domestic political figures \n", "2 domestic political figures \n", "3 domestic political figures \n", "4 domestic political figures " ], "text/html": "
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fakerealnotescategory
0Dumbo[5]Randolph Tex AllesDirector of the United States Secret Servicedomestic political figures
1Where's Hunter[6]Hunter BidenAmerican lawyer and lobbyist who is the second...domestic political figures
21% Joe[7] / Basement Joe[8] / Beijing Joe[9] /...Joe Biden47th vice president of the United States; form...domestic political figures
3Little Michael Bloomberg[19] / Mini Mike[20] /...Michael Bloomberg108th Mayor of New York City; 2020 Democratic ...domestic political figures
4Da Nang Richard[22] / Da Nang Dick[23]Richard BlumenthalU.S. senator from Connecticut; 23rd attorney g...domestic political figures
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" }, "metadata": {}, "execution_count": 1 } ], "source": [ "# packages\n", "import pandas as pd\n", "import seaborn as sns\n", "from wordcloud import WordCloud\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import re\n", "\n", "raw = pd.read_csv('data/trump.nicknames.wikipedia.csv')\n", "\n", "# categories I want to keep for visualizations and training\n", "# although all groups might serve better as there is already a low amount of data\n", "people_groups = ['domestic political figures', 'foreign leaders', 'media figures', 'other people']\n", "\n", "# filter for categories in the list\n", "raw = raw[raw['category'].isin(people_groups)]\n", "\n", "raw.head()" ] }, { "source": [ "# About the data\n", "\n", "A quick blurb about the data:\n", "\n", "+ fake = the list of unique nicknames given by Donald Trump\n", "+ real = the real name of the individual, group, or organization\n", "+ notes = descriptions of who the corresponding person is\n", "+ category = the category given by wikipedia\n", "\n", "Also, the categories were limited to those that are actual people, and groups of people. As this is going to be used for training a model on nickname generation, this makes the most sense.\n", "\n", "# First look\n", "\n", "The first thing I notice is that the fake names have reference numbers next to them, and are listed together. So let's create a single row for each fake name, and clean up both fake and real names for any special characters, and bring them to lowercase. We can however leave some characters to see how the model handels them, such as the \"1%\" nickname given, as it will add context.\n", "\n", "# Cleaning the data" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 2, "metadata": { "tags": [] }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " fake name real name len fake len real \\\n", "0 dumbo randolph tex alles 1 3 \n", "1 wheres hunter hunter biden 2 2 \n", "2 1% joe joe biden 2 2 \n", "3 basement joe joe biden 3 2 \n", "4 beijing joe joe biden 3 2 \n", "\n", " category \\\n", "0 domestic political figures \n", "1 domestic political figures \n", "2 domestic political figures \n", "3 domestic political figures \n", "4 domestic political figures \n", "\n", " notes \n", "0 director of the united states secret service \n", "1 american lawyer and lobbyist who is the second... \n", "2 47th vice president of the united states; form... \n", "3 47th vice president of the united states; form... \n", "4 47th vice president of the united states; form... " ], "text/html": "
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fake namereal namelen fakelen realcategorynotes
0dumborandolph tex alles13domestic political figuresdirector of the united states secret service
1wheres hunterhunter biden22domestic political figuresamerican lawyer and lobbyist who is the second...
21% joejoe biden22domestic political figures47th vice president of the united states; form...
3basement joejoe biden32domestic political figures47th vice president of the united states; form...
4beijing joejoe biden32domestic political figures47th vice president of the united states; form...
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" }, "metadata": {}, "execution_count": 2 } ], "source": [ "# defining two functions,\n", "# get_row() will take information and create a row,\n", "# then clean is used to clean the strings \n", "def clean(name):\n", " '''cleans the given name of special chs and lowers'''\n", " # deletes reference tag and lowers\n", " if '[' in name:\n", " name = name[0:name.find('[')]\n", " # replaces everything but whats in the string with a space\n", " name = re.sub(r\"[^a-z0-9%'.]\", \" \", name.lower())\n", " name = re.sub(r\"[^a-z0-9% ]\", \"\", name) # replace with null\n", "\n", " if name[0] == ' ': # no leading spaces\n", " name = name[1:]\n", "\n", " return name\n", "\n", "def get_row(fake, row):\n", " '''takes info and reproduces a row'''\n", " # pull data from row\n", " i, f, real, notes, category = row\n", "\n", " return {\n", " \"fake name\": clean(fake).strip(),\n", " \"real name\": clean(str(real)).lower().strip(),\n", " \"len fake\": len(fake.split(' ')),\n", " \"len real\": len(real.split(' ')),\n", " \"category\": str(category),\n", " \"notes\": str(notes).lower()\n", " }\n", "\n", "# flattening the listed nicknames\n", "cleanDF = pd.DataFrame([get_row(fake, row) # returns dictionary obj\n", " for row in raw.itertuples()\n", " for fake in str(row[1]).split('/')])\n", "\n", "cleanDF.head()\n", " " ] }, { "source": [ "As we can see with the head, most special characters are removed, everything is lowercase, and should be ready for training. I also added two columns for the word length of the fake and real names. Let's start by taking a look at the length comparisons.\n", "\n", "In the data there are also tags used '(denied by trump)', however these appear after the reference tag, and as such were deleted along with them. However there are a few issues that I hand corrected, first \"Ilhan Omar\", \"Ayanna Pressley\", \"Rashida Tlaib\" had NaN names, as they were originally grouped under one 'person', this was corrected. The name \"13/17 Angry Democrats\" has a '/' which was also used to seperate the names, so I changed this to another character that will get cleaned into a space. Lastly \"Randolph \"Tex\" Alles\" has quotation marks around his name, so I manually cleaned that as his name was the only case of it happening.\n", "\n", "One major change is that the data was originally formatted as 'wacky / weirdo / Tom Steyer' effectively changing only the adjective. Instead of attempting to create a set of rules to modify these and since the data is of low volume, I have gone into the data and added the full nickname as given by the sources on wikipedia. Consequently this will raise the word counts for the names in nickname vocabulary, and might effect the output of the generated nickname to include parts of names that are not correlated to the input names. However, that is something that can be dealt with later down the road if it becomes an issue in training.\n", "\n", "Now that all the names are cleaned lets move on to some basic statistics of the data.\n", "\n", "# Simple Statistics" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "162 97\nthe data shape: (162, 6)\nthe number of categories: 4\nthe number of people that recieved a name: 97\nthe number of Nicknames given by Trump: 162\nthe number of repeated unique names: 0\n" ] } ], "source": [ "# printing stats\n", "print(len(cleanDF[\"fake name\"].index), len(cleanDF[\"real name\"].unique()))\n", "print(f'the data shape: {cleanDF.shape}')\n", "print(f'the number of categories: {len(cleanDF[\"category\"].unique())}')\n", "print(f'the number of people that recieved a name: {len(cleanDF[\"real name\"].unique())}')\n", "print(f'the number of Nicknames given by Trump: {len(cleanDF[\"fake name\"].unique())}')\n", "print(f'the number of repeated unique names: {len(cleanDF[\"fake name\"].index) - len(cleanDF[\"fake name\"].unique())}')" ] }, { "source": [ "Awesome, we can see that there are 167 unique nicknames given by trump, to a total of 101 people. This means that every person recieved ~1.6 unique nicknames.\n", "\n", "Lets move onto some visualizations of Trumps Nicknaming Patterns.\n", "\n", "# Comparing lengths of nicknames and real names " ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "
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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "# creating a new column to just rename it easily\n", "cleanDF['count'] = 1\n", "# counting the number of relations between the lengths of words\n", "counts = cleanDF.groupby(['len real', 'len fake']).agg({'count':'count'}).reset_index()\n", "\n", "# graphing\n", "ax = sns.heatmap(counts.pivot_table(columns='len real', index='len fake', values='count'), annot=True, cmap='Wistia')\n", "ax.invert_yaxis()\n", "plt.title('Comparison of lengths\\nbetween Nicknames (fake) and Their Corrosponding Real names')\n", "plt.show()" ] }, { "source": [ "Awesome, now we can clearly see the relations between the length of the real names and length of the fake names! We see a trend to add words for real names with less than or equal to 3 words. However, Trumps Nickname word length is typically from 2 - 4 words, with 3 being the most frequent. When generating the names, if we wish to control the number of words in the nicknames, we can use these distributions to create pobabilities for the nickname lengths, based on the user given name!\n", "\n", "Next lets dive into our text! Here I want to get a sense of what sort of vocabulary there is to work with\n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "
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}, "metadata": { "needs_background": "light" } } ], "source": [ "# grabbing the counts of each vocabulary word in the nicknames\n", "fake_counts = (cleanDF['fake name'].str.split(expand=True)\n", " .stack()\n", " .value_counts()\n", " .rename_axis('vals')\n", " .reset_index(name='count'))\n", "\n", "# converting to a dictionary for graphing\n", "words = [word for word in fake_counts['vals']]\n", "n = [n for n in fake_counts['count']]\n", "weights = {words[i]: n[i] for i in range(len(n))}\n", "\n", "# custom color function because random coloring is bad\n", "def my_tf_color_func(word, **kwargs):\n", " w = 6 * weights[word]\n", " return f\"hsl(0, 96%, {110-w}%)\"\n", "\n", "#graphing\n", "wordcloud = WordCloud(width=1200, height=600, margin=0, relative_scaling=1, color_func=my_tf_color_func)\n", "\n", "wordcloud = wordcloud.generate_from_frequencies(weights)\n", "\n", "plt.imshow(wordcloud, interpolation='bilinear')\n", "plt.axis(\"off\")\n", "plt.margins(x=0, y=0)\n", "plt.title('Trumps Nickname Vocabulary')\n", "plt.show()" ] }, { "source": [ "Here we can see a wordcloud of the fake nicknames coined by trump, with the larger words appearing more frequently in the unique names. I also scaled the color of the words to represent the frequency as well, to make the more frequent words pop out more.\n", "\n", "Next I want to look into the count of words per person, to see who the most nicknamed people are! This one is just for fun!\n", "\n", "# Who was given the most nicknames?" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "
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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "# counting the number of unique names for each real name, then grabbing the top 10\n", "top10 = cleanDF.groupby('real name').agg({'fake name':'count'}).sort_values(by='fake name', ascending=False).reset_index().head(10)\n", "\n", "# graphing\n", "ax = sns.barplot(data=top10, x='real name', y='fake name')\n", "plt.xticks(rotation=70)\n", "plt.title('Number of Unique Nicknames Created by Trump')\n", "plt.show()" ] }, { "source": [ "Here we can see that Joe Biden has, by far, the most nicknames dubbed by Trump. Next up is James Comey, Nancy Pelosi, Adam Schiff, Hillary Clinton, and Elizabeth Warren. Although, do keep in mind this list is strictly the amount of unique names given. I attempted a search for the frequencies of Trump Nicknames, although did not find any easily avaible sources. I gotta say, pretty surprised Hillary Clinton didn't have the most, although I am sure she tops the frequency charts. Also Obama only had 1 coined nickname of \"cheatin' Obama\"\n", "\n", "Now I am going to export the data used to create the python graphs and re-create them in Tableau into a singular visualization.\n", "\n", "Most of the nicknames are ironic and seem to be projecting, like \"quid pro joe\", and there are a few that are just disgusting. None-the-less here is a show list of my favorite from the list." ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "the nicknames i like are: al frankenstein, quid pro joe, mini mike, lyin ted, moderate dog, nervous nancy, britain trump, mad alex, justin from canada, that guy on cbs, jeff bozo, ...\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ " nickname real name \\\n", "8 quid pro joe joe biden \n", "15 mini mike michael bloomberg \n", "39 lyin ted ted cruz \n", "47 al frankenstein al franken \n", "61 moderate dog james mattis \n", "79 nervous nancy nancy pelosi \n", "117 britain trump boris johnson \n", "123 mad alex alex salmond \n", "124 justin from canada justin trudeau \n", "131 that guy on cbs stephen colbert \n", "152 jeff bozo jeff bezos \n", "\n", " notes \n", "8 47th vice president of the united states; form... \n", "15 108th mayor of new york city; 2020 democratic ... \n", "39 former solicitor general of texas; u.s. senato... \n", "47 former u.s. senator from minnesota \n", "61 26th secretary of defense \n", "79 speaker of the united states house of represen... \n", "117 prime minister of the united kingdom \n", "123 former first minister of scotland and scottish... \n", "124 23rd prime minister of canada \n", "131 host of the late show with stephen colbert; co... \n", "152 founder, chairman, ceo, and president of amazo... " ], "text/html": "
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nicknamereal namenotes
8quid pro joejoe biden47th vice president of the united states; form...
15mini mikemichael bloomberg108th mayor of new york city; 2020 democratic ...
39lyin tedted cruzformer solicitor general of texas; u.s. senato...
47al frankensteinal frankenformer u.s. senator from minnesota
61moderate dogjames mattis26th secretary of defense
79nervous nancynancy pelosispeaker of the united states house of represen...
117britain trumpboris johnsonprime minister of the united kingdom
123mad alexalex salmondformer first minister of scotland and scottish...
124justin from canadajustin trudeau23rd prime minister of canada
131that guy on cbsstephen colberthost of the late show with stephen colbert; co...
152jeff bozojeff bezosfounder, chairman, ceo, and president of amazo...
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" }, "metadata": {}, "execution_count": 7 } ], "source": [ "# some meme programing cause why not\n", "class people:\n", " def __init__(self, like_list):\n", " self.i_like = like_list \n", "\n", " def __str__(self):\n", " return f'the nicknames i like are: {\", \".join(self.i_like)}, ...'\n", "\n", "pDF = cleanDF[['fake name', 'real name', 'notes']]\n", "print(p := people('al frankenstein,quid pro joe,mini mike,lyin ted,moderate dog,nervous nancy,britain trump,mad alex,justin from canada,that guy on cbs,jeff bozo'.split(',')))\n", "pDF[pDF['fake name'].isin(p.i_like)].rename(columns={'fake name':'nickname'})" ] }, { "source": [ "# Is this data good enough for training?\n", "\n", "The data seems to be well diverse, with a healthy amount of adjectives. Although there is not a whole lot of nicknames to train from, but luckily as accuracy is not an issue, the whole dataset can be used for training.\n", "\n", "One idea of increasing the data size for training is to incorporate synonyms for the adjectives used. As well as some data hallucination techniques to mix the adjectives around to different people. One major note is having the actual names inside the training data, I am hoping this teaches the model to include the user name, however if thats not possible it might be best to just generate the prefix to a name.\n", "\n", "Another idea is to use a hand-built algorithm if the neural model does not perform well. Although I am thinking a LSTM character (or word) based generation model will perform well with this data, however there is truly only one way to find out!" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# exporting data for tableau visualizations\n", "top10.to_csv('data/top.10.people.nicknamed.csv', index=False) # bar chart\n", "fake_counts.to_csv('data/nickname.word.counts.csv', index=False) # wourd frequency\n", "counts.to_csv('data/heatmap.name.lengths.csv', index=False) # heatmap\n", "cleanDF.to_csv('data/cleaned.nicknames.csv', index=False) # cleaned df" ] } ] }