- Axel Strubel
- Avi Barazani
- Yaniv Ben-Malka
Using machine learning tools, we have built a supervised nlp-based jokes regressor, that gets an English joke as an input, creates various features that try to capture the sense of the joke, and predicts how funny it is, in a scale 0 to 100.
A reddit jokes dataset, that contains more than 180,000 jokes and their score (votes).
Our future intention is to use the scrap tweets from a pre-made list of 'funny' pages, and use the tweets to further train & test the model, in addition to some more twitter-based feature engineering.
Notebooks
- EDA
- Feature Engineering
- Feature Processing (Dimensionality Reduction)
- Modeling
Joke_Predictor
- predict_joke.py (Run the entire pipline for predicting an input joke score)
Data
- reddit_jokes.json - Dataset currently in use
Twitter_API (Not in use)
- Get_Tweets.py - Tweets scraper
- tweets.csv - A dataset of scraped tweets
docs.google.com/document/d/1f_8DQBT13SHORA22K4gOfqOf_l_9iXPtuVgdrXE55Cw