Sentiment Analysis of Bengali Textual Comments in Field of Sports Using Deep Learning Approach

In recent days, people are expressing their emotions, feelings or opinions on various social platforms. In those opinions some are real and some are fake. There are a lot of discussions about sports. When their team wins a match, they celebrate this highly but when a match loses, they criticize, bullying them. And then they express them angrily to different sites, like Facebook pages, Facebook groups etc. This issue may be resolved by using natural language processing (NLP) to analyze the sentiment of the relevant comments. Here we analyze sentiment in various sports related Bangla comments. We collected almost 4061 data from various Facebook pages and groups. After collecting those data, we classified them into five different categories: neutral, happy, sad, positive and negative. We use some preprocessing techniques like removing punctuation, data cleaning, manual validation to prepare our data. In this study, we used three different familiar deep learning models to predict sentiment of our dataset. Here our models are CNN, LSTM and BiLSTM. In these three models CNN with the glove word embedding performed better than other two models, and it is 94.57%. Finally, the CNN model outperforms other models in a way that captures the sentiment of the fans' remarks.