Sentiment Classification in Bengali News Comments using a hybrid approach with Glove

Sentiment analysis (SA) is a greater part of Natural Language Processing (NLP) in the research field. Previously a lot of work was done by SA in various languages. Most of the work was done in the English language. A little work was done in the Bengali language and it's increasing day by day. Nowadays, Bangla News comments are very demanding for research work in the Bangla language. The procedures of text categorization, classifying, and different techniques for extracting features in textual information were discussed in this paper. Some Bangla newspaper dataset is currently available in online platforms. This research study has analyzed Bangla news comments sentiment using a hybrid approach and a pre-trained deep learning classifier. The proposed hybrid model utilizes an optimizer function “Adam” along with a word embedding “Glove”. The dataset utilized in the proposed model is collected from online platform Kaggle, the largest data science forum on the world. This dataset contains 13802 data. Using five different classes to our dataset. For building a model, this research study has applied preprocessing techniques, which makes a significant contribution in cleaning our dataset. This leads to get a well formed dataset. The proposed hybrid model has been combined with two familiar deep learning methods, namely BiLSTM and CNN. By comparing these two methods, the proposed hybrid model has gained better accuracy than the FastText model. The accuracy of hybrid model is about 89.89%.