Deep Learning Based Sentiment Analysis from Bangla Text Using Glove Word Embedding along with Convolutional Neural Network

Abstract

Language is the primary means by which humans interact with each other and express their emotions. Despite the fact that teaching robots to understand textual meaning is challenging, it is not entirely impossible because of Natural Language Processing (NLP), a branch of machine learning (ML) that allows for the analysis of the meaning of spoken or written words. The field of ML has evolved quite a bit in English. Even though English-based NLP systems have been extremely successful in many fields, other languages are also being used for this crucial function. In contrast, hardly much work has been done on Bengali Text. This study was based on several Bangla texts that were categorized into three: positive, negative, and neutral. Glove word embedding, Adam optimizer, and CNN's deep learning classifier along with Glove-BiLSTM. Glove-CNN were utilized to assure an accurate result from the data obtained, and GLove+CNN acquired an accuracy of 99.43%.