A Reddit Flair Detector system to detect flairs (category) of India subreddit submissions (posts). The application has been deployed online using Heroku at Reddit Flair Detector.

The directory is a Flask web application set-up for hosting on Heroku servers. The description of files and folders can be found below: .
├── data-scrape
│   └── scrape.py
├── main.py
├── Procfile
├── README.md
├── requirements.txt
└── templates
     └── main.html

I started off with text cleaning. I found that the target feature ‘link_flair_css_class’ contains lots of junk. I cleaned it. Label encode the target class. Merged the ‘Self text’ with ‘title’. Convert the the time to timestamp. Created following Sklearn model

• Tree Based Model
  • DecisionTreeClassifier
  • ExtraTreeClassifier
• Neighbors Model
  • KNeighborsClassifier
• Ensemble Model
  • GradientBoostingClassifier
  • RandomForestClassifier
  • ExtraTreesClassifier

and used f1 score as metrics and build above model on following features:

• Bag of words count
• Tfidf count
• tfidf_vect_ngram_chars ngram(2,3)

got similar score of around 55 f1 score. I also created post tag features:

• noun count
• verb_count
• Adj count
• Pron count
• Word density
• Punctuation count

Then I realised that I’m not exploiting the text semantic in above model. I decide to use Deep Neural Network with Seq to Seq learning with multi head architecture. The best thing about this is that now I don’t need to extensive features engineering. Deep Neural Network is strong enough to learn features in its own. I used three input head to feed input as detailed below:

• Title and Self text merge together
• Full Link
• Non text features
  • Label encoded Domain
  • StandardScale Score
  • StandardScale Number of Comments
  • is_video
  • is_crosspostable
  • is_self
  • over_18
  • parent_whitelist_status
  • send_replies
  • Timestamp extracted features i.e. month, day, weekday, hours

Other features which we got it from Reddit API were dropped as they don’t have much variability . I cleaned and normalised the test before feeding into embedding layer. I used Stanford Glove embedding. Initially I used ‘glove.840B.300d.txt' but I was not able to save model ‘coz of size of non trained parameter. Then I decide to use ‘glove.twitter.27B.100d.txt’ . I choose this because it close to Reddit post textual data.

I used most frequent word to decide the size of the Vocabulary for word embedding. This took lot of time to adjust to teh size which mu laptop can handle data. I limit the text sequence size to 200. I used GRU model as it has less parameter than LSTM so it will train quickly with limited time. I used 5 epoch to train the model and saved the best model.