Rodolfo Ferro

Posted on Sep 12, 2017 • Edited on Nov 24, 2018

Sentiment analysis on Trump's tweets using Python 🐍

#twitter #python #tweepy #textblob

Oldest comments (97)

Ben Halpern • Sep 12 '17

Fascinating. I wouldn't be surprised if this kind of research goes mainstream in the future in journalism.

Rodolfo Ferro • Sep 12 '17

Exactly, I think about the same. Or for having good impact in your products ( social media) if you're trying to sell stuff. There are many possibilities.

A project in mind is to just work in this kind of analysis for suicide prevention. 👍

Lasse Schultebraucks • Sep 12 '17

Nice, very interesting! He seems to tweet surprisingly a high count of positive tweets (51%). But how much of this tweets are fake news and lies is another question... nytimes.com/interactive/2017/06/23...

Rodolfo Ferro • Sep 12 '17 • Edited

Yeah, that resulted surprising to me! I've heard that he's not the only one tweeting from his account, but he has a team for this. That might be a possible reason. That's why it results interesting to analyze the polarity of tweets that come from different sources.

Murtaza Haji • Apr 2 '18

Well technically these sentiment calculations should be taken with a grain of salt. you use VaderSentiment library as well and compare both values of sentiments to get better insight.

Pepe Párraga • Sep 14 '17

Excelente trabajo Rodolfo para NLP. Saludos un abrazo

Rodolfo Ferro • Sep 14 '17

Muchísimas gracias. Como mencionaba en el post, en mi Github puede encontrarse el notebook con el contenido en español (por cualquier cosa).

¡Saludos!

Alex Singleton • Sep 14 '17

Hi there, I was having some trouble with the "visualizing the statistics" section as detailed in sections 2.1 and 2.2; if you take a look at my GitHub repo, you'll notice I had to comment out # %matplotlib inline and replaced requirement with plt.ion() within the script-running file (trumpet.py) in order to run the scripts without failure (e.g. python3 trumpet.py). Can you please explain how to generate the visualizations as detailed in those sections? For some reason, I'm unable to render those visual within my Jupyter Notebook-env/config. I'm only 10 days new to Python, so I'd appreciate any guidance. Great tutorial-
thanks!

Rodolfo Ferro • Sep 15 '17

Sure! It's quite easy actually. :)

Instead of adding plt.ion() at the beginning, you can add the following code each time you're generating a plot, in order to visualize it: plt.show(). This will open an external window and display the immediately last plot generated.

You can see this in the Official Pyplot tutorial I shared at the end (References).

Please let me know I you have any other problem. :)

Alex Singleton • Sep 15 '17

Got it, Rodolfo! Thank you for the guidance- tremendous fun! ;)

Ibrahimkasim • Sep 16 '17

Awesome tutorial!!

Rodolfo Ferro • Sep 16 '17

Thank you so much! 😀👍🏼

Florian Rohrer • Sep 16 '17

Thank you for your tutorial! Its was easy to follow and everything work on my first attempt!

I do not want to reload all the tweets from the web, while I am developing. I altered the first few lines, to cache the tweets locally.

save = "saved.pickle"
if os.path.exists(os.path.join(os.path.dirname(__file__), save)):
    with open(save, 'rb') as f:
        tweets = pickle.load(f)
else:
    extractor = twitter_setup()
    tweets = extractor.user_timeline(screen_name="realDonaldTrump", count=200)
    with open(save, 'wb') as f:
        pickle.dump(tweets, f)

Rodolfo Ferro • Sep 16 '17 • Edited

Excellent idea!

What I did at the end (in my personal case) was to save the tweet list as a csv file (data.to_csv(...)), taking as an advantage that I already had all the info in a pandas dataframe. :)

Thanks for your great comment!

Jude • Sep 17 '17

Nicely done. I had installed Anaconda before but didn't really get past Hello World in the Jupyter notebook. This was an excellent idea to get people like me off their proverbial rear-end and use it for a very fun idea! I was able to follow it right through and get everything to work after dusting off the cobwebs of my Anaconda environment.

Thanks for sharing!

Rodolfo Ferro • Sep 17 '17

Thank you so much! I really appreciate it.

I'll try to keep posting stuff like this, I enjoy doing applied things with Python. :)

Rodolfo Ferro • Sep 25 '17

One of my ideas about this post is to give tools to implement solutions on different areas. As you say, this could help in healthcare analysis. For that you might need a specific classifier (not the texblob's default I used), and you can learn how to build one in the last reference I provide in the post.

If you begin working on that, please let us know if there's a thing on which we may help.

Best!

Fred. • Sep 27 '17 • Edited

Would it be possible to check / detect how many likes comes from the staff of a VIP ? It is said that many politicals manage likes and retweets by asking their support to like and retweet their messages? (not sure to be clear) Through 200 tweets, this would be possible to look at the twitter accounts that like systematically and quickly (as soon as published, like bots do) then substract (or minimize) them from the final evaluation.

Rodolfo Ferro • Sep 27 '17

This is an interesting question.

If you want to count something like this in real time, you would need to modify the way you're consuming the API (rest) and create a listener (you can still do that with Tweepy). That's what I would do, I'd create a specific listener for Trump's tweets and use threads to count for certain time likes and retweets for a new tweet.

Does this answer help? I can try to be more explicit. :)