Sarcasm detection is the process of identifying whether a piece of text is sarcastic or not. Sarcasm is one of the main challenges for sentiment analysis systems. This is because a sarcastic sentence usually carries a negative implicit sentiment, while it is expressed using positive expressions. This contradiction between the surface sentiment and the intended one creates a complex challenge for sentiment analysis systems. Sarcasm detection received attention in other languages, but Bengali still lags behind. There is a broader business and educational interest in developing a Bengali AI tool to identify sarcastic content. In this regard, We, Department of Electronics and Telecommunication Engineering (ETE), CUET, is elated to present an NLP competition, as a part of ETE DecaHertz 2022.
Competetion link: https://www.kaggle.com/competitions/nlp-competition-cuet-ete-day-2022/overview
Leaderboard: https://www.kaggle.com/competitions/nlp-competition-cuet-ete-day-2022/leaderboard
From label distribution, we can see that the dataset is imbalanced. As deep learning model can handle imbalanced dataset, we didn't need to use upsampling and downsampling technique.
If we do downsampling , our model will lose vast amount of potential Data. If we do upsampling, our model will lose it generalization ability.
From frequency length distribution, we can find out the maximum,minimum and average length of our training data which will guide us during Tokenization and Model creation phase.
Here, I've lately fused four model to build a Fusion model. No word embedding is used. Models I've used:
- BiGRU (Bidirectional Gated Recurrent Unit)
- BiLSTM (Bidirectional Long Short Term Memory)
- BiGRU + CNN (Hybrid Model)
- BiLSTM + CNN (Hybrid Model)
