Topic Modeling — Time Series

In these lessons, we’re learning about a text analysis method called topic modeling. This method will help us identify the main topics or discourses within a collection of texts or single text that has been separated into smaller text chunks.

Dataset

Trump’s Tweets

I hear that Fake News CNN just reported that I am isolated in the White House wondering out loud “when will life return to normal?” Does anybody really believe that?

—Donald Trump, March 25, 2020 tweet

In this particular lesson, we’re going to use Little MALLET Wrapper, a Python wrapper for MALLET, to topic model a CSV file of Donald Trump’s tweets and plot the fluctuation of topics over time. The Trump tweets dataset is downloaded from The Trump Twitter Archive.


If you're working in this Jupyter notebook on your own computer, you'll need to have both the Java Development Kit and MALLET pre-installed. For set up instructions, please see the previous lesson.

If you’re working in this Jupyter notebook in the cloud via Binder, then the Java Development Kit and Mallet will already be installed. You’re good to go!

Set MALLET Path

Since Little MALLET Wrapper is a Python package built around MALLET, we first need to tell it where the bigger, Java-based MALLET lives.

We’re going to make a variable called path_to_mallet and assign it the file path of our MALLET program. We need to point it, specifically, to the “mallet” file inside the “bin” folder inside the “mallet-2.0.8” folder.

path_to_mallet = 'mallet-2.0.8/bin/mallet'

If MALLET is located in another directory, then set your path_to_mallet to that file path.

Install Packages

!pip install little_mallet_wrapper
!pip install seaborn

Import Packages

Now let’s import the little_mallet_wrapper and the data viz library seaborn.

import little_mallet_wrapper
import seaborn
import glob
from pathlib import Path
import pandas as pd
import random
pd.options.display.max_colwidth = 100

We’re also going to import glob and pathlib for working with files and the file system, as well as pandas for working with the CSV data.

Get Training Data From CSV File

Before we topic model Donald Trump’s tweets, we need to process the tweets and prepare them for analysis. The steps below demonstrate how to process texts if they come from a CSV file.

Note

We’re calling these text files our training data, because we’re training our topic model with these texts. The topic model will be learning and extracting topics based on these texts.

Trump Tweets

This dataset of Donald Trump’s tweets is taken from Trump Twitter Archive. To read in the CSV file, we’re going to use Pandas.

Pandas

Do you need a refresher or introduction to the Python data analysis library Pandas? Be sure to check out Pandas Basics (1-3) in this textbook!

trump_df = pd.read_csv("../texts/social-media/Trump-Tweets_2009-2021.csv", encoding='utf-8')
trump_df.head()
id text isRetweet isDeleted device favorites retweets date isFlagged
0 1.347570e+18 To all of those who have asked, I will not be going to the Inauguration on January 20th. f f Twitter for iPhone 510761 83532 1/8/21 15:44 f
1 1.347560e+18 The 75,000,000 great American Patriots who voted for me, AMERICA FIRST, and MAKE AMERICA GREAT A... f f Twitter for iPhone 480997 108844 1/8/21 14:46 f
2 1.347330e+18 https://t.co/csX07ZVWGe f f Twitter Media Studio 629326 154877 1/8/21 0:10 f
3 1.346950e+18 These are the things and events that happen when a sacred landslide election victory is so uncer... f f Twitter for iPhone 0 0 1/6/21 23:01 f
4 1.346930e+18 https://t.co/Pm2PKV0Fp3 f f Twitter for iPhone 0 0 1/6/21 21:17 f
trump_df['text'] = trump_df['text'].astype(str)

Process Trump Tweets

training_data = [little_mallet_wrapper.process_string(text, numbers='remove') for text in trump_df['text']]

Get Original Trump Tweets

original_trump_tweets = [title for title in trump_df['text']]

Get Dataset Statistics

little_mallet_wrapper.print_dataset_stats(training_data)
Number of Documents: 56571
Mean Number of Words per Document: 12.0
Vocabulary Size: 59952

Training the Topic Model

We’re going to train our topic model with the little_mallet_wrapper.train_topic_model() function.

So we have to set a few things up first.

Set Number of Topics

We need to make a variable num_topics and assign it the number of topics we want returned.

num_topics = 35

Set Training Data

We already made a variable called training_data, which includes all of our processed Reddit post texts, so we can just set it equal to itself.

training_data = training_data

Set Other MALLET File Paths

Then we’re going to set a file path where we want all our MALLET topic modeling data to be dumped. I’m going to output everything onto my Desktop inside a folder called “topic-model-output” and a subfolder specific to the Reddit posts called “Reddit.”

All the other necessary variables below output_directory_path will be automatically created inside this directory.

#Change to your desired output directory
output_directory_path = 'topic-model-output/Trump-Tweets'

#No need to change anything below here
Path(f"{output_directory_path}").mkdir(parents=True, exist_ok=True)

path_to_training_data           = f"{output_directory_path}/training.txt"
path_to_formatted_training_data = f"{output_directory_path}/mallet.training"
path_to_model                   = f"{output_directory_path}/mallet.model.{str(num_topics)}"
path_to_topic_keys              = f"{output_directory_path}/mallet.topic_keys.{str(num_topics)}"
path_to_topic_distributions     = f"{output_directory_path}/mallet.topic_distributions.{str(num_topics)}"

Train Topic Model

Then we’re going to train our topic model with little_mallet_wrapper.quick_train_topic_model(). The topic model should take about 1-1.5 minutes to train and complete. If you want it, you can look at your Terminal or PowerShell and see what the model looks like as it’s training.

little_mallet_wrapper.quick_train_topic_model(path_to_mallet,
                                                                     output_directory_path,
                                                                     num_topics,
                                                                     training_data)
Importing data...
Complete
Training topic model...
Complete
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  ['obamacare',
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   'cold'],
  ['trump',
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   'realdonaldtrump',
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   'club',
   'trumpdoral',
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   'miami',
   'amazing',
   'world',
   'blue',
   'turnberry',
   'new'],
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   'justice',
   'states',
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   'united',
   'president',
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   'full',
   'barrett',
   'kavanaugh',
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  ['border',
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   'crime',
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   'laws',
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  ['http',
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   'realdonaldtrump',
   'hotel',
   'tower',
   'chicago',
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   'york',
   'pageant'],
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  ['election',
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Display Topics and Top Words

topics = little_mallet_wrapper.load_topic_keys(path_to_topic_keys)

for topic_number, topic in enumerate(topics):
    print(f"✨Topic {topic_number}\n\n{topic}\n")
✨Topic 0✨

['democrats', 'impeachment', 'president', 'schiff', 'house', 'pelosi', 'nancy', 'senate', 'https', 'nothing', 'call', 'adam', 'dems', 'jim', 'said', 'jordan', 'whistleblower', 'ukraine', 'democrat', 'realdonaldtrump']

✨Topic 1✨

['jobs', 'economy', 'record', 'ever', 'history', 'years', 'market', 'since', 'best', 'time', 'numbers', 'year', 'unemployment', 'great', 'stock', 'high', 'president', 'new', 'economic', 'https']

✨Topic 2✨

['news', 'fake', 'media', 'story', 'cnn', 'new', 'people', 'nytimes', 'even', 'times', 'never', 'failing', 'made', 'dishonest', 'bad', 'york', 'many', 'totally', 'reporting', 'like']

✨Topic 3✨

['realdonaldtrump', 'trump', 'president', 'run', 'donald', 'would', 'need', 'country', 'vote', 'please', 'america', 'thanks', 'needs', 'great', 'thank', 'make', 'true', 'man', 'get', 'one']

✨Topic 4✨

['great', 'realdonaldtrump', 'congratulations', 'team', 'game', 'nfl', 'win', 'http', 'yankees', 'https', 'last', 'time', 'night', 'job', 'guy', 'rod', 'football', 'proud', 'big', 'players']

✨Topic 5✨

['realdonaldtrump', 'apprentice', 'celebapprentice', 'apprenticenbc', 'show', 'season', 'celebrity', 'great', 'nbc', 'tonight', 'love', 'celebrityapprentice', 'wait', 'star', 'http', 'watch', 'thanks', 'back', 'night', 'see']

✨Topic 6✨

['obamacare', 'tax', 'bill', 'healthcare', 'americans', 'american', 'care', 'senate', 'taxes', 'plan', 'must', 'today', 'act', 'cut', 'republicans', 'cuts', 'https', 'obama', 'congress', 'big']

✨Topic 7✨

['https', 'mike', 'pence', 'global', 'warming', 'office', 'post', 'realdonaldtrump', 'mini', 'old', 'promises', 'climate', 'change', 'bloomberg', 'washington', 'president', 'record', 'made', 'kept', 'cold']

✨Topic 8✨

['trump', 'http', 'golf', 'course', 'realdonaldtrump', 'great', 'doral', 'scotland', 'national', 'best', 'club', 'trumpdoral', 'thanks', 'beautiful', 'miami', 'amazing', 'world', 'blue', 'turnberry', 'new']

✨Topic 9✨

['court', 'supreme', 'justice', 'states', 'judge', 'united', 'president', 'https', 'case', 'federal', 'decision', 'judges', 'trump', 'justices', 'full', 'barrett', 'kavanaugh', 'amy', 'legal', 'senate']

✨Topic 10✨

['border', 'wall', 'security', 'immigration', 'illegal', 'democrats', 'mexico', 'country', 'must', 'southern', 'want', 'borders', 'crime', 'people', 'laws', 'open', 'build', 'stop', 'street', 'congress']

✨Topic 11✨

['http', 'trump', 'realdonaldtrump', 'hotel', 'tower', 'chicago', 'miss', 'great', 'new', 'thanks', 'building', 'trumpchicago', 'city', 'best', 'beautiful', 'sign', 'vegas', 'universe', 'york', 'pageant']

✨Topic 12✨

['great', 'endorsement', 'total', 'complete', 'vote', 'strong', 'https', 'state', 'military', 'amendment', 'vets', 'second', 'governor', 'job', 'crime', 'congressman', 'loves', 'senator', 'big', 'people']

✨Topic 13✨

['hillary', 'would', 'clinton', 'obama', 'said', 'people', 'crooked', 'years', 'like', 'never', 'last', 'bad', 'debate', 'campaign', 'many', 'one', 'even', 'bush', 'bernie', 'president']

✨Topic 14✨

['ratings', 'foxnews', 'show', 'bad', 'really', 'cnn', 'like', 'realdonaldtrump', 'chris', 'morning', 'joe', 'dumb', 'total', 'watch', 'rosie', 'magazine', 'got', 'fired', 'television', 'much']

✨Topic 15✨

['people', 'get', 'country', 'great', 'would', 'like', 'many', 'never', 'much', 'time', 'want', 'back', 'done', 'going', 'good', 'know', 'even', 'big', 'democrats', 'let']

✨Topic 16✨

['great', 'realdonaldtrump', 'prime', 'minister', 'ties', 'macys', 'new', 'thanks', 'trump', 'macy', 'general', 'secretary', 'chief', 'https', 'japan', 'shirts', 'http', 'best', 'congratulations', 'thank']

✨Topic 17✨

['https', 'trump', 'poll', 'new', 'thank', 'realdonaldtrump', 'makeamericagreatagain', 'approval', 'gop', 'republican', 'rating', 'party', 'cruz', 'http', 'polls', 'iowa', 'numbers', 'foxnews', 'hampshire', 'carson']

✨Topic 18✨

['realdonaldtrump', 'great', 'thanks', 'thank', 'good', 'book', 'nice', 'trump', 'happy', 'new', 'https', 'luck', 'one', 'job', 'really', 'best', 'true', 'birthday', 'read', 'wonderful']

✨Topic 19✨

['think', 'big', 'entrepreneurs', 'like', 'keep', 'success', 'work', 'never', 'best', 'business', 'deal', 'always', 'focus', 'one', 'realdonaldtrump', 'give', 'good', 'make', 'way', 'every']

✨Topic 20✨

['realdonaldtrump', 'trump', 'thanks', 'true', 'love', 'like', 'donald', 'truth', 'people', 'never', 'twitter', 'losers', 'haters', 'know', 'would', 'best', 'one', 'mark', 'sugar', 'say']

✨Topic 21✨

['election', 'https', 'vote', 'votes', 'ballots', 'fraud', 'voting', 'state', 'voter', 'mail', 'win', 'ballot', 'georgia', 'rigged', 'states', 'won', 'republican', 'pennsylvania', 'presidential', 'people']

✨Topic 22✨

['https', 'great', 'america', 'make', 'thank', 'vote', 'get', 'let', 'together', 'trump', 'keep', 'maga', 'going', 'work', 'makeamericagreatagain', 'co/u', 'hard', 'safe', 'realdonaldtrump', 'never']

✨Topic 23✨

['china', 'trade', 'united', 'states', 'deal', 'korea', 'great', 'many', 'countries', 'north', 'dollars', 'tariffs', 'billion', 'president', 'much', 'world', 'good', 'jobs', 'back', 'made']

✨Topic 24✨

['https', 'american', 'america', 'today', 'people', 'nation', 'president', 'women', 'thank', 'great', 'whitehouse', 'country', 'day', 'realdonaldtrump', 'god', 'honor', 'men', 'every', 'united', 'americans']

✨Topic 25✨

['http', 'obama', 'barackobama', 'cont', 'iran', 'china', 'oil', 'isis', 'syria', 'mittromney', 'iraq', 'attack', 'debt', 'nuclear', 'deal', 'america', 'middle', 'gas', 'russia', 'president']

✨Topic 26✨

['https', 'house', 'white', 'great', 'today', 'president', 'whitehouse', 'thank', 'honor', 'happy', 'realdonaldtrump', 'day', 'meeting', 'conference', 'flotus', 'first', 'forward', 'trump', 'melania', 'press']

✨Topic 27✨

['interview', 'tonight', 'http', 'foxnews', 'enjoy', 'foxandfriends', 'great', 'fox', 'interviewed', 'watch', 'show', 'realdonaldtrump', 'morning', 'discussing', 'seanhannity', 'night', 'cnn', 'debate', 'news', 'trump']

✨Topic 28✨

['law', 'police', 'enforcement', 'people', 'mayor', 'https', 'radical', 'order', 'new', 'city', 'federal', 'left', 'york', 'portland', 'cities', 'must', 'violence', 'attack', 'officers', 'families']

✨Topic 29✨

['covid', 'wind', 'coronavirus', 'hurricane', 'https', 'people', 'fema', 'state', 'new', 'help', 'federal', 'local', 'turbines', 'testing', 'alexsalmond', 'government', 'safe', 'storm', 'florida', 'california']

✨Topic 30✨

['fbi', 'witch', 'hunt', 'collusion', 'trump', 'mueller', 'russia', 'campaign', 'https', 'report', 'clinton', 'hillary', 'comey', 'obama', 'democrats', 'investigation', 'russian', 'crooked', 'james', 'president']

✨Topic 31✨

['https', 'realdonaldtrump', 'thank', 'live', 'president', 'whitehouse', 'trump', 'danscavino', 'teamtrump', 'maga', 'watch', 'news', 'true', 'co/h', 'coronavirus', 'co/l', 'co/d', 'co/o', 'potus', 'force']

✨Topic 32✨

['https', 'great', 'thank', 'carolina', 'big', 'see', 'crowd', 'rally', 'tomorrow', 'forward', 'join', 'night', 'trump', 'tonight', 'soon', 'north', 'florida', 'people', 'maga', 'iowa']

✨Topic 33✨

['http', 'trump', 'via', 'donald', 'breitbartnews', 'https', 'media', 'newsmax', 'read', 'article', 'obama', 'says', 'gop', 'tea', 'exclusive', 'speech', 'party', 'mac', 'cpac', 'wsj']

✨Topic 34✨

['biden', 'joe', 'https', 'sleepy', 'president', 'american', 'realdonaldtrump', 'vote', 'left', 'trump', 'democrats', 'china', 'america', 'teamtrump', 'radical', 'would', 'hunter', 'black', 'energy', 'wins']

Display Top Tweets For Topic

Load Topic Distributions

topic_distributions = little_mallet_wrapper.load_topic_distributions(path_to_topic_distributions)
tweet_dict = dict(zip(training_data, original_trump_tweets))
def display_top_tweets_per_topic(topic_number=0, number_of_documents=5):
    
    print(f"✨Topic {topic_number}\n\n{topics[topic_number]}\n")

    for probability, document in little_mallet_wrapper.get_top_docs(training_data, topic_distributions, topic_number, n=number_of_documents):
        print(round(probability, 4), tweet_dict[document] + "\n")
    return
display_top_tweets_per_topic(topic_number=1, number_of_documents=5)
✨Topic 1✨

['jobs', 'economy', 'record', 'ever', 'history', 'years', 'market', 'since', 'best', 'time', 'numbers', 'year', 'unemployment', 'great', 'stock', 'high', 'president', 'new', 'economic', 'https']

0.9608 Stock Market up almost 40% since the Election, with 7 Trillion Dollars of U.S. value built throughout the economy. Lowest unemployment rate in many decades, with Black &amp, Hispanic unemployment lowest in History, and Female unemployment lowest in 21 years. Highest confidence ever!

0.9563 “The unemployment rate remains at a 17-year low of 4.1%. The unemployment rate in manufacturing dropped to 2.6%, the lowest ever recorded. The unemployment rate among Hispanics dropped to 4.7%, the lowest ever recorded...”@SecretaryAcosta @USDOL https://t.co/vou28s0yHN

0.9433 Would be sooo great if the Fed would further lower interest rates and quantitative ease. The Dollar is very strong against other currencies and there is almost no inflation. This is the time to do it. Exports would zoom!

0.9433 Incredible number just out, 7,036,000 job openings. Astonishing - it’s all working! Stock Market up big on tremendous potential of USA. Also, Strong Profits. We are Number One in World, by far!

0.9334 TAX CUTS will increase investment in the American economy and in U.S. workers, leading to higher growth, higher wages, and more JOBS! https://t.co/4GxM8psMU1

Show Topic Words in Context of Full Tweet

from IPython.display import Markdown, display
import re

def display_bolded_topic_words_in_context(topic_number=3, number_of_documents=3, custom_words=None):

    print(f"✨Topic {topic_number}\n\n{topics[topic_number]}\n")

    for probability, document in little_mallet_wrapper.get_top_docs(training_data, topic_distributions, topic_number, n=number_of_documents):

        probability = f"✨✨✨\n\n**{probability}**"
        original_text = tweet_dict[document]
        original_text_lowered = original_text.lower()
        topic_words = topics[topic_number]
        topic_words = custom_words if custom_words != None else topic_words

        for word in topic_words:
            if word in original_text_lowered:
                original_text = re.sub(f"\\b{word}\\b", f"**{word.upper()}**", original_text, flags=re.I)

        display(Markdown(probability)), display(Markdown(original_text))
    return
display_bolded_topic_words_in_context(topic_number=1, number_of_documents=4)
✨Topic 1✨

['jobs', 'economy', 'record', 'ever', 'history', 'years', 'market', 'since', 'best', 'time', 'numbers', 'year', 'unemployment', 'great', 'stock', 'high', 'president', 'new', 'economic', 'https']

✨✨✨

0.9607886969575177

STOCK MARKET up almost 40% SINCE the Election, with 7 Trillion Dollars of U.S. value built throughout the ECONOMY. Lowest UNEMPLOYMENT rate in many decades, with Black &amp, Hispanic UNEMPLOYMENT lowest in HISTORY, and Female UNEMPLOYMENT lowest in 21 YEARS. Highest confidence EVER!

✨✨✨

0.9562956605511918

“The UNEMPLOYMENT rate remains at a 17-YEAR low of 4.1%. The UNEMPLOYMENT rate in manufacturing dropped to 2.6%, the lowest EVER recorded. The UNEMPLOYMENT rate among Hispanics dropped to 4.7%, the lowest EVER recorded...”@SecretaryAcosta @USDOL HTTPS://t.co/vou28s0yHN

✨✨✨

0.9433021986807756

Would be sooo GREAT if the Fed would further lower interest rates and quantitative ease. The Dollar is very strong against other currencies and there is almost no inflation. This is the TIME to do it. Exports would zoom!

✨✨✨

0.9433021986807756

Incredible number just out, 7,036,000 job openings. Astonishing - it’s all working! STOCK MARKET up big on tremendous potential of USA. Also, Strong Profits. We are Number One in World, by far!

Plot Topics Over Time

Load Topic Distributions

topic_distributions = little_mallet_wrapper.load_topic_distributions(path_to_topic_distributions)

Add Topic Distribution Columns

Add column with all topic distributions for every tweet

trump_df['topic_distributions'] = pd.Series(topic_distributions)

Make a separate dataframe with each topic distribution as a separate column

topic_distributions_df = trump_df['topic_distributions'].apply(pd.Series)

Rename each of those columns with the first four words from the topic

topic_distributions_df.columns = [" ".join(topic[:4]) for topic in topics]

Merge that column into the dataframe

trump_df = pd.concat([trump_df, topic_distributions_df], axis=1)

Date Formatting For Time Series Plot

Convert to datetime

trump_df['date'] = pd.to_datetime(trump_df['date'])

Extract year

trump_df['year'] = pd.to_datetime(trump_df['date'].dt.year, format='%Y')

Extract year and month

trump_df['year-month'] = trump_df['date'].dt.to_period('M')
trump_df['Date (by month)'] = [month.to_timestamp() for month in trump_df['year-month']]

Set year and month as Pandas Index

trump_df = trump_df.set_index('Date (by month)')

Plot Individual Topics as Time Series

topic_number = 10

topic_label = " ".join(topics[topic_number][:4])

trump_df[topic_label].plot(style='.', title=f'Trump Tweets By Topic: \n{topic_label.title()}')
<AxesSubplot:title={'center':'Trump Tweets By Topic: \nBorder Wall Security Immigration'}, xlabel='Date (by month)'>
../_images/11-Topic-Modeling-Time-Series_81_1.png
topic_number = 10

topic_label = " ".join(topics[topic_number][:4])

trump_df.groupby(trump_df.index)[[topic_label]].mean().plot(title=f'Trump Tweets By Topic: \n{topic_label.title()}', linewidth=2)
<AxesSubplot:title={'center':'Trump Tweets By Topic: \nBorder Wall Security Immigration'}, xlabel='Date (by month)'>
../_images/11-Topic-Modeling-Time-Series_82_1.png
topic_number = 21

topic_label = " ".join(topics[topic_number][:4])

trump_df.groupby(trump_df.index)[[topic_label]].mean().plot(title=f'Trump Tweets By Topic: \n{topic_label.title()}', linewidth=2)
<AxesSubplot:title={'center':'Trump Tweets By Topic: \nElection Https Vote Votes'}, xlabel='Date (by month)'>
../_images/11-Topic-Modeling-Time-Series_83_1.png
topic_number = 30

topic_label = " ".join(topics[topic_number][:4])

trump_df.groupby(trump_df.index)[[topic_label]].mean().plot(title=f'Trump Tweets By Topic: \n{topic_label.title()}', linewidth=2)
<AxesSubplot:title={'center':'Trump Tweets By Topic: \nFbi Witch Hunt Collusion'}, xlabel='Date (by month)'>
../_images/11-Topic-Modeling-Time-Series_84_1.png
topic_number = 9

topic_label = " ".join(topics[topic_number][:4])

trump_df.groupby(trump_df.index)[[topic_label]].mean().plot(title=f'Trump Tweets By Topic: \n{topic_label.title()}', linewidth=2)
<AxesSubplot:title={'center':'Trump Tweets By Topic: \nCourt Supreme Justice States'}, xlabel='Date (by month)'>
../_images/11-Topic-Modeling-Time-Series_85_1.png
topic_number = 34

topic_label = " ".join(topics[topic_number][:4])

trump_df.groupby(trump_df.index)[[topic_label]].mean().plot(title=f'Trump Tweets By Topic: \n{topic_label.title()}', linewidth=2)
<AxesSubplot:title={'center':'Trump Tweets By Topic: \nBiden Joe Https Sleepy'}, xlabel='Date (by month)'>
../_images/11-Topic-Modeling-Time-Series_86_1.png
topic_number = 23

topic_label = " ".join(topics[topic_number][:4])

trump_df.groupby(trump_df.index)[[topic_label]].mean().plot(title=f'Trump Tweets By Topic: \n{topic_label.title()}', linewidth=2)
<AxesSubplot:title={'center':'Trump Tweets By Topic: \nChina Trade United States'}, xlabel='Date (by month)'>
../_images/11-Topic-Modeling-Time-Series_87_1.png