CNN LSTM Hybrid Approach for Sentiment Analysis

Abstract

Abstract: In recent years, one of the most popular study subjects has been sentiment analysis. It is employed to ascertain the text's actual intention. It is primarily interested in the processing and analysis of natural language data. The development of technology and the phenomenal rise of social media have produced a vast volume of confusing textual information. It's critical to examine the feelings that underlie such writings. Sentiment analysis reveals the core of irrational beliefs kept in enormous volumes of text. The primary objective is to get the computer to comprehend the backdrop of the data so that it may be divided into material that is good or bad. (i) Several machine learning models, including Naive Bayes, XGboost, Random Forest, LGB Machine, etc., are trained in this study. (ii) The implementation of the deep learning model Bi-LSTM, whose accuracy has showed promise. (iii) Bidirectional Encoder Representations from Transformers (BERT), a pre-trained language model that used an external Bi-LSTM model, was implemented. Then, a new approach of CNN-LSTM hybrid model is applied to IMDb dataset which performed better than all the models.