Sentiment Analysis of Movie Reviews Based on LSTM-Adaboost
Ling Zhang, Miao Wang, Ming Liu, Haozhan Li · 2022 IEEE 5th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC) · 2022
With the improvement of public living standards and the rapid development of the Internet, film works as cultural carriers have become an integral part of people's cultural and spiritual life. The booming movie industry has also given rise to a large number of user online reviews. How to effectively identify the positive and negative emotional tendencies of movie reviews is of great significance to the online word-of-mouth research and publicity marketing of movies. Based on the machine learning perspective of deep learning, this paper proposes a LSTM-Adaboost text sentiment analysis method which combines LSTM neural network and Adaboost boosting method. And the experiments are compared with CNN and LSTM models on the IMDB movie review dataset. The results show that the proposed method in this paper has higher accuracy compared with CNN and LSTM methods. In terms of sentiment classification accuracy, the LSTM-Adaboost method improves by 6 percentage points.