Sentiment analysis of Bangla language using a new comprehensive dataset BangDSA and the novel feature metric skipBangla-BERT

In this modern technologically advanced world, Sentiment Analysis (SA) is a very important topic in every language due to its various trendy applications. But SA in Bangla language is still in a dearth level. This work focuses on examining different hybrid feature extraction techniques and learning algorithms on Bangla Document level Sentiment Analysis using a new comprehensive dataset (BangDSA) of 203,493 comments collected from various microblogging sites. The proposed BangDSA dataset approximately follows the Zipf’s law, covering 32.84% function words with a vocabulary growth rate of 0.053, tagged both on 15 and 3 categories. In this study, we have implemented 21 different hybrid feature extraction methods including Bag of Words (BOW), N-gram, TF-IDF, TF-IDF-ICF, Word2Vec, FastText, GloVe, Bangla-BERT etc with CBOW and Skipgram mechanisms. The proposed novel method (Bangla-BERT＋Skipgram), skipBangla-BERT outperforms all other feature extraction techniques in machine leaning (ML), ensemble learning (EL) and deep learning (DL) approaches. Among the built models from ML, EL and DL domains the hybrid method CNN-BiLSTM surpasses the others. The best acquired accuracy for the CNN-BiLSTM model is 90.24% in 15 categories and 95.71% in 3 categories. Friedman test has been performed on the obtained results to observe the statistical significance. For both real 15 and 3 categories, the results of the statistical test are significant.