WCSE

Sentiment Analysis of Movie Review Based on LSTM

Abstract— This paper introduces a sentiment analysis model based on LSTM (Long Short-Term Memory), which can be used for emotional polarity classification of movie review data. The model consists of five levels, namely input layer, embedding layer, LSTM layer, softmax layer, and output layer. IMDb (Internet Movie Database) dataset is used for training, and SGD (stochastic gradient descent), Adadelta, and RMSprop for optimization. The experiment result shows that Adadelta optimization algorithm can get the best training model and the lowest prediction error rate.

Index Terms— LSTM, Deep Learning, Sentiment Analysis, Polarity Classification.

Yuyao Cheng
Faculty of Education, The University of Hong Kong, HONGKONG
Qiyan Kang, Yongfen Liu, Li Cheng
Jinshan College, Fujian Agriculture and Forestry University, CHINA
Changying Wang
College of Computer and Information Sciences, Fujian Agriculture and Forestry University, CHINA

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Cite: Yuyao Cheng, Qiyan Kang, Changying Wang, Yongfen Liu, Li Cheng, "Sentiment Analysis of Movie Review Based on LSTM," Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering, pp. 280-287, Hong Kong, 15-17 June, 2019.