I decided to publish my solution of corresponding competitions on Kaggle in this repository. The main idea of most notebooks is to extract new features to build ensemble in future.

As hashtags are popular in social networks, I decided to use them as features. During exploring I found that intersect of tags in train and in test sets isn't big. I tried to use stemming and lemmatization, but it didn't give anything. To build vector representation of each row I used CountVectorizer from sklearn. It was founded that the KNN can give as much more score than Random Forest on these features. You can find more details in the notebook.

When I get a pretty bad result in the previous notebook, I decided that the main reason for it was small intersect of tags. So I used pre-trained GloVe vectors from Twitter. As some of the tweets have more than one tag, I used 'Math with Words' to get one vector. Using this approach, I raised the metric by 8 points and I used the same trick for locations. In this case, the KNN gave better results. More details.

To get more features from tweets I used Vader. Vader (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media. I expected to get a good correlation between new features and target, but it was not so. Only on this data, Random Forest was a little bit better than KNN. More details.

I just use pre-trained bert from SimpleTransformers. During some time of training it, I found good hyperparameters and found that better to use minimal data preprocessing. When I picked predictions of all models I realized, that predictions for Bert were in the wrong order. I think that the reason of this is a bug and after spending 2 days to fix it I didn’t succeed. So I decided to use an average of Bert's prediction and predictions that I plan to get from stacking. More details.

To build a good ensemble I decided to use not pretrained Net with pretrained GloVe vectors. At this time, I tried just one model that I gave not bad predictions for sarcasm detection, but I want to try some other architectures for this task. The notebook with this approach. The model's script here.

Since the competition is not over yet, I plan to do:

• Build ensemble.
• Use FastAi's pre-trained LSTM.