Alyssa Fu

Insight Demo slides for textfit

Transcript

1. textfit.me   How  will  your  comment  fit  in  the  community?   Alyssa  Fu   Insight Data Science
2. the  algorithm   Data  Source   520k comments from 4k posts Data  Cleaning   Recency, comment length, comment level Sentiment  Analysis   Bag of words Positive + Negative Logistic  Regression   Predict the probability of being upvoted vs. downvoted Webapp   Classification of comment based on pre-trained model Real-time Reddit API calls
3. sentiment  analysis   Comments in /r/GirlGamers are more positive than in /r/politics
4. sentiment  and  fit   Comments that were downvoted had more negative sentiment
5. sentiment  fit  by  subreddit   Comments that are downvoted have lower sentiment (even for subreddits with mostly negative sentiment)
6. validation   Overall Accuracy: 68.3% (Compare to No-Information Rate: 52.2%, p < .001) Predicted   Upvotes   Downvotes   Actual   Upvotes   Downvotes   Upvote   Recall:  60%   Downvote   Recall:  75.8%  
7. about  me  –  Alyssa  Fu  
8. recency   The earlier comments are made, the wider the possible range of upvotes and downvotes
9. Back and forth conversations are more likely on /r/politics (ps < .001) DBDude 0 points 1 month ago No, I completely countered your statement that a one-degree change… onetime 0 points 1 month ago Please, quote me saying that exact statement. I said that, changes to the… DBDude 0 points 1 month ago You just keep going down the rabbit hole, don’t you? You claim funds… Onetime 1 point 1 month ago That’s not what I said at all. That’s why I asked you to quote me… [+] DBDude 0 points 1 month ago (35 children)