Tag Archives: nltk

Coding

What Donald Trump is Tweeting (Analyzing Tweets with NLTK and Pandas)

How does @realDonaldTrump (Donald J. Trump) tweet? Before the President-elect became the president-elect, I didn’t pay much attention to his tweets, but I did know that he seemed to have a unique style of writing them. To me, it felt like he tweeted like he spoke in public. But what was that tone or Trump brand?

From a quick glance of his account, the usage of exclamation points seemed prominent. I wondered if that was consistent, and I wondered what else I could find. So I ran an analysis Trump’s tweets as well as @HillaryClinton, @CNN and @FoxNews’ for some comparison.

My strategy for selecting the accounts:

  1. Find another individual user’s account with opposing views, but who had some similar goals over the last year + (to be come President of the United States)
  2. Compare the individual users’ accounts with “objective” news sources’ Twitter accounts. Since source objectivity is always a topic of hot debate (or a hot topic of debate), I took @CNN and @FoxNews.

The Method

  • Pull as many of Trump’s Tweets as I could via Twitter API. The best service to perform this operation is /user_timeline. You can only get the last 3,200 tweets from any particular user handle, which is unfortunate because plenty of accounts have authored far more than 3,200 tweets (e.g. Trump has written over 30,000 tweets).
  • Take the tweets and do some Natural Language Processing with Python’s NLTK.
    • Get the parts of speech tags for every word in every tweet
    • Do word frequency counts
    • Classify the sentiment of the tweets
  • Classify the reading level or difficulty of each word. Determine the reading level of the account. 
    • I didn’t implement this. I started, but then I got distracted.
  • Pull out an aggregate view of some other interesting tweet data, like #HASHTAG usage.
  • Compare data.

And Now, The Data

Punctuation! Punctuation! Punctuation! Trumpunctuation?

If you’ve seen Trump speak, you probably may have noticed his strong intonations and general emphatic demeanor. His tweets seem to capture this partially through punctuation alone. His words are sprinkled with the strongest punctuation mark in the English language, the exclamation mark.

“!” occurs 2336 times over 3200 Trump tweets. 

And “!” occurs at least once 1954 times over 3200 Trump tweets.

Which means that 61% of Trump’s tweets contain an exclamation mark (based on my 3200 tweet dataset). This seemed astonishingly high; when I first pulled this I thought my code was incorrect. So I opened my file of tweets and eyeballed it to confirm. I figured if 60+ % of tweets contained an exclamation mark, then it would be easy to confirm my sanity (or lack thereof) from the text file.

trump's usage of exclamation mark

Some of Trump’s Tweets

The highest frequency of “!” in a single tweet was five. And that occurred in the following tweet:

“#WheresHillary? Sleeping!!!!!”
Which was Retweeted 27,158 times and Favorited 61,084 times. Created on August 20, 2016.

biggest_excla_tweet

Here’s the !!!! comparison among the entire group:

@realDonaldTrump Absolute Count (!) @HillaryClinton Absolute Count (!) @CNN Absolute Count (!) @FoxNews Absolute Count (!)
2336 171 43 134

Adjectives Are Very Great

Trump’s top twenty favorite adjectives are listed below, along with the top 20 adjectives from the comparison group. It’s not really a surprise that “great” was number one for Trump because, among other things, his campaign’s slogan was “Make America Great Again.” Notably, I removed stopwords and only grabbed lowercase words to help filter out some noise from the data. Relative frequency is the percentage relative to the other adjectives in the dataset.

trump_word abs_frequency rel_frequency
great 211 4.953052
bad 81 1.901408
many 81 1.901408
big 77 1.807512
last 66 1.549296
new 56 1.314554
good 50 1.173709
much 34 0.798122
amazing 32 0.751174
total 30 0.704225
wonderful 30 0.704225
nice 27 0.633803
massive 25 0.586854
first 25 0.586854
presidential 23 0.539906

 

Below is the list of top 20 adjectives from @realDonaldTrump, @HillaryClinton, @CNN, @FoxNews from their last 3200 tweets.

adj_comparison

 

General Sentiment of Tweets

To calculate the sentiment of the tweets, I used a function in the nltk library called demo_liu_hu_lexicon() which classifies each word of the sentence as Positive, Negative, or Neutral, and then does a basic count of each word-classification category. Whichever group has the highest count is how the text will get assigned. There are definitely better ways to do this. I considered an integration with IBM’s Watson, but time was doing its thing, being time, and being of the essence and such.

@realDonaldTrump @HillaryClinton @CNN @FoxNews
Positive 0.463750 0.468750 0.304688 0.235625
Neutral 0.285313 0.401875 0.425938 0.455937
Negative 0.250937 0.129375 0.269375 0.308437

Top Hashtags

This section contains a top 5 hashtag summary table for the entire analysis group, and also has the top 20 hashtags for each account listed afterward. You can infer what you will from this data.

I did find it interesting that the top-used hashtag by Trump was one of self-promotion, and Hillary Clinton used a lot of hashtags relating to the debates. Just looking at the hashtag data makes me think that Trump’s social media strategy was much stronger throughout the campaign.

In addition, all four Twitter accounts had at least one hashtag with “Trump” in it. From a marketing perspective, that’s good brand awareness.

Top 5 Hashtag Summaries

@realDonaldTrump Top 5 Hashtags @HillaryClinton Top 5 Hashtags @CNN Top 5 Hashtags @FoxNews Top 5 Hashtags
#Trump2016, #MakeAmericaGreatAgain, #MAGA, #AmericaFirst, #DrainTheSwamp #DebateNight, #DemsInPhilly, #VPDebate, #RNCinCLE, #debate #CNNHeroes, #CNNSOTU, #Aleppo, #CNNNYE, #JadonAndAnias #KellyFile, #Trump, #FoxNews2017, #Hannity, #Christmas

@realDonaldTrump Hashtags

abs_frequency hashtag rel_frequency_pct
238 Trump2016 15.130324
202 MakeAmericaGreatAgain 12.841704
111 MAGA 7.056580
79 AmericaFirst 5.022250
78 DrainTheSwamp 4.958678
57 ImWithYou 3.623649
57 BigLeagueTruth 3.623649
53 VoteTrump 3.369358
38 CrookedHillary 2.415766
36 Debate 2.288620
35 TrumpTrain 2.225048
34 TrumpPence16 2.161475
24 Debates2016 1.525747
22 ICYMI 1.398601
20 SuperTuesday 1.271456
18 VPDebate 1.144310
15 RNCinCLE 0.953592
14 Debates 0.890019
13 WIPrimary 0.826446
12 ThankYouTour2016 0.762873

Below are the top hashtags for the entire group.
all_hashtags

It’s also worth sharing how many total hashtags were used in the last 3200 tweets of each Twitter account in the dataset group:

@realDonaldTrump @HillaryClinton @CNN @FoxNews
total_hashtags_used 1573 482 143 810

 

Top Favorited Tweets

Top Favorited of @realDonaldTrump

text favorite_count created_at
Such a beautiful and important evening! The forgotten man and woman will never be forgotten again. We will all come together as never before 639428 Wed Nov 09 11:36:58 +0000 2016
TODAY WE MAKE AMERICA GREAT AGAIN! 577008 Tue Nov 08 11:43:14 +0000 2016
How long did it take your staff of 823 people to think that up–and where are your 33,000 emails that you deleted? https://t.co/gECLNtQizQ 296236 Thu Jun 09 20:40:32 +0000 2016
The media is spending more time doing a forensic analysis of Melania\’s speech than the FBI spent on Hillary\’s emails. 248370 Wed Jul 20 15:36:06 +0000 2016
Just had a very open and successful presidential election. Now professional protesters, incited by the media, are protesting. Very unfair! 234619 Fri Nov 11 02:19:44 +0000 2016
Love the fact that the small groups of protesters last night have passion for our great country. We will all come together and be proud! 224497 Fri Nov 11 11:14:20 +0000 2016
Nobody should be allowed to burn the American flag – if they do, there must be consequences – perhaps loss of citizenship or year in jail! 216064 Tue Nov 29 11:55:13 +0000 2016
Fidel Castro is dead! 212487 Sat Nov 26 13:08:11 +0000 2016
This will prove to be a great time in the lives of ALL Americans. We will unite and we will win, win, win! 204239 Sat Nov 12 15:05:33 +0000 2016
A fantastic day in D.C. Met with President Obama for first time. Really good meeting, great chemistry. Melania liked Mrs. O a lot! 194927 Fri Nov 11 02:10:46 +0000 2016

 

Top Favorited of @HillaryClinton

text favorite_count created_at
“I never said that.” \u2014Donald Trump, who said that. #debatenight https://t.co/6T8qV2HCbL 160098 Tue Sep 27 01:19:47 +0000 2016
Where was this kind of comedy last night? https://t.co/71JhLG55G5 134956 Fri Oct 21 17:51:34 +0000 2016
“Trump just criticized me for preparing for this debate. You know what else I prepared for? Being president.” #DebateNight 112647 Tue Sep 27 02:01:59 +0000 2016
Women have the power to stop Trump.\\n\\nhttps://t.co/tTgeqy51PU\\nhttps://t.co/VH3woeAf9Q https://t.co/NjvbkPsjPR 111546 Fri Oct 07 23:54:35 +0000 2016
“Nobody respects women more than me.” \u2014Donald Trump earlier tonight\\n\\n”Such a nasty woman.” \u2014Donald Trump just now #DebateNight 108114 Thu Oct 20 02:36:56 +0000 2016
Don\’t stand still. Vote today: https://t.co/jfd3CXLD1s #ElectionDay #MannequinChallenge https://t.co/4KAv2zu0rd 95882 Tue Nov 08 11:47:18 +0000 2016
Happy birthday to this future president. https://t.co/JT3HiBjYdj 94281 Wed Oct 26 13:03:18 +0000 2016
RT if you\u2019re proud of Hillary tonight. #DebateNight #SheWon https://t.co/H7CJep7APX 93121 Thu Oct 20 02:37:43 +0000 2016
RT this if you\’re proud to be standing with Hillary tonight. #debatenight https://t.co/91tBmKxVMs 92721 Tue Sep 27 02:45:44 +0000 2016
This is horrific. We cannot allow this man to become president. https://t.co/RwhW7yeFI2 91471 Fri Oct 07 20:55:13 +0000 2016

 

Top Favorited of @CNN

text favorite_count created_at
At least 60 people have been hurt in an explosion at a fireworks market near Mexico City, local media report.\u2026 https://t.co/2UWK6ECpsf 22570 Tue Dec 20 22:49:24 +0000 2016
This little boy is the newest face of OshKosh B\’gosh\’s holiday ads after initially being turned down by a talent ag\u2026 https://t.co/bdc1Zig7lE 22506 Sat Dec 10 07:01:29 +0000 2016
“We\’ve been friends for a long time”: Kanye West and President-elect Trump appear together at Trump Tower\u2026 https://t.co/KIdFwkVTGr 19082 Tue Dec 13 14:59:55 +0000 2016
This little boy is the newest face of OshKosh B\’gosh\’s holiday ads after initially being turned down by a talent ag\u2026 https://t.co/8ppZdDs3Te 12099 Sat Dec 10 23:45:25 +0000 2016
Clinton jokes at Reid portrait unveiling: “After a few weeks of taking selfies in the woods, I thought it would be\u2026 https://t.co/zpVwUAnjId 11868 Thu Dec 08 22:23:56 +0000 2016
BREAKING: President Obama vows retaliatory action against Russia for its meddling in the US presidential election\u2026 https://t.co/SJPGxWQB8d 9767 Fri Dec 16 01:36:31 +0000 2016
More Americans voted for Hillary Clinton than any other losing presidential candidate in US history https://t.co/6TBbQi3Nsn 9273 Wed Dec 21 23:28:00 +0000 2016
This little boy is the newest face of OshKosh B\’gosh\’s holiday ads after initially being turned down by a talent ag\u2026 https://t.co/LG0rdFFVkc 9117 Mon Dec 12 00:42:57 +0000 2016
Mariah Carey, Adele, Elton John and Lady Gaga bring the holiday spirit in special Christmas-themed \’Carpool Karaoke\u2026 https://t.co/8aQ5BDIbKt 7475 Mon Dec 19 05:30:24 +0000 2016
\’Dear world, why are you silent?\’: Desperate pleas from inside Aleppo https://t.co/1RvPlSDB7a https://t.co/XYwbJibFTD 7355 Wed Dec 14 19:00:04 +0000 2016

 

Top Favorited of @FoxNews

text favorite_count created_at
JUST IN: President-elect #Trump announces @Sprint will bring 5,000 jobs back to the U.S., and OneWeb will hire 3,00\u2026 https://t.co/CVWrTyuFNR 18974 Wed Dec 28 22:17:35 +0000 2016
.@JudgeJeanine: “Michelle, you may not realize it, but Americans rejected you and everything you stand for.”\u2026 https://t.co/tGrzIlZjxN 15054 Sun Dec 18 17:17:51 +0000 2016
#Breaking News: President-elect @realDonaldTrump has garnered the 270 #ElectoralCollege votes needed to become pres\u2026 https://t.co/5CDoiZCs4d 11164 Mon Dec 19 22:35:53 +0000 2016
Peters: “Without the least exaggeration, we can say that President Obama has been the worst foreign policy presiden\u2026 https://t.co/JbIGCpwKUk 10263 Sat Dec 31 03:32:46 +0000 2016
.@realDonaldTrump: “Michelle Obama said yesterday that there\’s no hope, but I assume she was talking about the past\u2026 https://t.co/V1BuKztapK 10021 Sat Dec 17 22:43:31 +0000 2016
.@realDonaldTrump: “We have to protect Israel. Israel, to me, is very, very important. We have to protect Israel.” https://t.co/R8CZWsGfvX 10021 Sun Jan 01 03:26:17 +0000 2017
Giuliani: \u201cThe U.S. Constitution doesn\u2019t give anyone in this world the right to come to the U.S. That\u2019s a privilege\u2026 https://t.co/TyKS7REAPR 8885 Wed Dec 21 03:41:24 +0000 2016
.@KatrinaPierson: This president is the divider-in-chief. His entire political career revolved around racism, sexis\u2026 https://t.co/vQP27Mxc1B 8700 Fri Dec 30 01:43:04 +0000 2016
.@GovMikeHuckabee: Can you name me one Muslim country that welcomes Christians to build & protect churches? No, you\u2026 https://t.co/baLyxGAkL6 8419 Thu Dec 29 01:39:42 +0000 2016
DJT: “I think the Democrats are putting it out because they suffered 1 of the greatest defeats in the history of po\u2026 https://t.co/2bBtHDwqu3 8175 Sun Dec 11 19:08:47 +0000 2016

About the Data

If you would like to access some of the code/data, it is publicly available on my GitHub repo. I’ve also included all four files that contain all of the tweets on which I ran the analysis in the data/ directory.

I had plans to include Date/Time data analysis in this post and many other things (if you’d like to see more data, let me know), but you have to stop somewhere great!!!!!! #MakeCodingProjectsSmallAgain

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Coding

How to Retrieve and Analyze Your iOS Messages with Python, Pandas and NLTK

I’m one of those people that keeps every text message I send or receive — I never delete them. Meet a girl at a bar, text her the next day and never hear back from her? I keep that. Weird wrong-number texts? I keep those too. Ex-girlfriend texts? Definitely keepers.

I had 65,378 messages on my phone at the time of writing this post.

I’m not a digital hoarder or anything, but I primarily do this because I like the idea of being able to search through the past. But, digital hoarder or not, collecting anything takes up some sort of space, and when I found that my text messages were taking up 4GBs of space on my phone, I decided it was time to back them up. It was at that point that I realized I could also probably analyze them.

As it turns out, you can do this, and I’ll tell you how. For this project, I used Python/Pandas/NLTK for the analysis and an iPython Notebook to render the datasets. I’ve also uploaded the code to GitHub, which you can view here.

An overview of the steps to make this happen:

  1. Sync/back up your iPhone because the messages need to be stored on your computer.
  2. Load the SQLite file and retrieve all messages
    • You can follow the directions for retrieving the right file here.
  3. Analyze those mensajes (I used Pandas)!!

Let’s get into some details.

You need to sync and back up your phone’s contents to your computer. There’s a great post on how to do this here. In case you want to skip that read, you’re ultimately getting a file with the text messages in it; copying it and moving it into your working directory.

You can find the file with this bash command:

$ find / -name 3d0d7e5fb2ce288813306e4d4636395e047a3d28

Now, loading the SQLite file — you can actually see what’s in this file via the command line:

 $ sqlite3 3d0d7e5fb2ce288813306e4d4636395e047a3d28

Then you can check out the available tables:

sqlite> .tables
_SqliteDatabaseProperties chat_message_join
attachment handle
chat message
chat_handle_join message_attachment_join

From here, the main tables I found useful were “message” and “handle.” The former contains all of your text messages, and the latter contains all of the senders/recipients. I only wrote code around the messages table, primarily because I could never figure out how to make a join between message and handle, but that was probably something trivial that I overlooked. Please tell me how you did it, if you did!

Continuing on, the message table has lots of columns in it, and I chose to select from the following:

['guid', 'service', 'text', 'date', 'date_delivered', 
'handle_id', 'type', 'is_read','is_sent', 'is_delivered',
'item_type', 'group_title']

The key field is “text,” which is where the content of the message is stored, which includes emojis! (A cool thing is that your emojis will show up if you try to plot them in something like an iPython notebook. You could run an entire analysis on emoji usage…)

My analysis, however, ultimately breaks down into two pieces:

  1. Analyzing the content of the “text” field (excluding emojis).
  2. Analyzing the messages themselves (for example, total text messages, or, what I sent vs. what I received, for instance).

For #1, I wrote code that:

  • Classifies all words and assigns a part of speech to them, then check the counts of each part of speech.

    • You should get a table looking like this.

      You should get a table looking like this.

  • Counts the number of times each word appears in the dataset, and gives an overview of the dataset:
    • total_words_filtered
  • Excludes boring words, like prepositions, and words that are < 2 characters.
  • Classifies all words as is_bad=1 or 0. I did this by using a .txt file full of bad words, found here:
  • Plots usage of bad words
    • I’d love to show you my plot, but let’s just assume I never swear…

For #2, the code allows you to:

  • Plot the number of text messages received each day (check out the spike on your birthday or during holidays). You can see my data below has a huge gap (that’s when my phone was replaced and not backed up for many months. My timestamp conversions are also apparently incorrect, but I haven’t looked into it.

    • The timestamp conversion is off, so someone can fix that... we're not in 2016, yet... Are we??

      The timestamp conversion is off, so someone can fix that… we’re not in 2016, yet…Or am I??

  • Count the number of sent versus received messages.

Anyway, I hope you can get some use out of this, and instead of blabbing on about the code here, I’ll just let you read it and use it on your own. Please check out my git repo, and please reach out to me with questions, comments, etc.

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