Movie sentiment analysis based on Long Short-Term Memory Network

Research Article
Open access

Movie sentiment analysis based on Long Short-Term Memory Network

Siyao Li 1 , Rui Qin 2* , Zijian Zhou 3
  • 1 Harbin Engineering University    
  • 2 Guangdong University of Technology    
  • 3 Central South University    
  • *corresponding author 3220000984@mail2.gdut.edu.cn
Published on 7 February 2024 | https://doi.org/10.54254/2755-2721/38/20230524
ACE Vol.38
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-301-2
ISBN (Online): 978-1-83558-302-9

Abstract

An important task in the study of Natural Language Processing (NLP) is the analysis of movie reviews. It finishes the task of classifying movie review texts into sentiment, such as positive, negative or neutral sentiment. Previous works mainly follow the pipeline of LSTM (Long Short-Term Memory Network). The network model is a variant of Recurrent Neural Network (RNN) and particularly suitable for processing natural language texts. Though existing LSTM-based works have improved the performance significantly, we argue that most of them deal with the problem of analyzing the sentiment of movie reviews while ignore analyze the model performance in different application scenarios, such as different lengths of the reviews and the frequency of sentiment adverbs in the reviews. To alleviate the above issue, in this paper, we constructed a simple LSTM model containing an embedding layer, a batch normalization layer, a dropout layer, a one-dimensional convolutional layer, a maximal pooling layer, a bi-directional LSTM layer and a fully connected layer. We used the existing IMDB movie review dataset to train the model, and selected two research scenarios of movie review length and frequency of occurrence of sentiment adverbs to test the model, respectively. From the experimental results, we proposed a model for the scenarios in which the LSTM model handles the problem of sentiment analysis with respect to the dataset construction, model stability and generalization ability, text fragment processing, data preprocessing and feature extraction, model optimization and improvement.

Keywords:

Sentiment Analysis, LSTM, Movie Review, Different Application Scenarios

Li,S.;Qin,R.;Zhou,Z. (2024). Movie sentiment analysis based on Long Short-Term Memory Network. Applied and Computational Engineering,38,16-25.
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References

[1]. Ullah K, Rashad A, Khan M, Ghadi Y, Aljuaid H, Nawaz Z. A Deep Neural Network-Based Approach for Sentiment Analysis of Movie Reviews. Complexity. 2022;2022. doi:https://doi.org/10.1155/2022/5217491

[2]. Chen, C., Xu, B., Yang, J. H., & Liu, M. (2022). Sentiment analysis of animated film reviews using intelligent machine learning. Computational Intelligence and Neuroscience, 2022.

[3]. Ghosh, S., & Ahammed, M. T. (2022). Effects of sentiment analysis on feedback loops between different types of movies. Journal of Media, Culture and Communication (JMCC) ISSN: 2799-1245, 2(02), 14-20.

[4]. L. Zhang, M. Wang, M. Liu and H. Li, "Sentiment Analysis of Movie Reviews Based on LSTM-Adaboost," 2022 IEEE 5th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Chongqing, China, 2022, pp. 186-190, doi: 10.1109/IMCEC55388.2022.10019969.

[5]. M. Mishra and A. Patil, "Sentiment Prediction of IMDb Movie Reviews Using CNN-LSTM Approach," 2023 International Conference on Control, Communication and Computing (ICCC), Thiruvananthapuram, India, 2023, pp. 1-6, doi: 10.1109/ICCC57789.2023.10165155.

[6]. S. M. Qaisar, "Sentiment Analysis of IMDb Movie Reviews Using Long Short-Term Memory," 2020 2nd International Conference on Computer and Information Sciences (ICCIS), Sakaka, Saudi Arabia, 2020, pp. 1-4, doi: 10.1109/ICCIS49240.2020.9257657.

[7]. Rehman, A.U., Malik, A.K., Raza, B. et al. A Hybrid CNN-LSTM Model for Improving Accuracy of Movie Reviews Sentiment Analysis. Multimed Tools Appl 78, 26597–26613 (2019). https://doi.org/10.1007/s11042-019-07788-7 .

[8]. Ranjith, V., Barick, R., Pallavi, C.V., Sandesh, S., Raksha, R. (2023). Sentiment Enhanced Smart Movie Recommendation System. In: Shakya, S., Balas, V.E., Haoxiang, W. (eds) Proceedings of Third International Conference on Sustainable Expert Systems . Lecture Notes in Networks and Systems, vol 587. Springer, Singapore. https://doi.org/10.1007/978-981-19-7874-6_4.

[9]. S. Namitha, P. Sanjan, N. C. Reddy, Y. Srikar, H. Shanmugasundaram and B. P. Andraju, "Sentiment Analysis: Current State and Future Research Perspectives," 2023 7th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 2023, pp. 1115-1119, doi: 10.1109/ICICCS56967.2023.10142318.

[10]. Rameshwer Singh, Rajeshwar Singh, Applications of sentiment analysis and machine learning techniques in disease outbreak prediction – A review, Materials Today: Proceedings,Volume 81, Part 2, 2023, Pages 1006-1011, ISSN 2214-7853, https://doi.org/10.1016/j.matpr.2021.04.356. (https://www.sciencedirect.com/science/article/pii/S2214785321032764)

[11]. H. Ge, S. Zheng and Q. Wang, "Based BERT-BiLSTM-ATT Model of Commodity Commentary on The Emotional Tendency Analysis," 2021 IEEE 4th International Conference on Big Data and Artificial Intelligence (BDAI), Qingdao, China, 2021, pp. 130-133, doi: 10.1109/BDAI52447.2021.9515273.

Cite this article

Li,S.;Qin,R.;Zhou,Z. (2024). Movie sentiment analysis based on Long Short-Term Memory Network. Applied and Computational Engineering,38,16-25.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-301-2(Print) / 978-1-83558-302-9(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.38
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Ullah K, Rashad A, Khan M, Ghadi Y, Aljuaid H, Nawaz Z. A Deep Neural Network-Based Approach for Sentiment Analysis of Movie Reviews. Complexity. 2022;2022. doi:https://doi.org/10.1155/2022/5217491

[2]. Chen, C., Xu, B., Yang, J. H., & Liu, M. (2022). Sentiment analysis of animated film reviews using intelligent machine learning. Computational Intelligence and Neuroscience, 2022.

[3]. Ghosh, S., & Ahammed, M. T. (2022). Effects of sentiment analysis on feedback loops between different types of movies. Journal of Media, Culture and Communication (JMCC) ISSN: 2799-1245, 2(02), 14-20.

[4]. L. Zhang, M. Wang, M. Liu and H. Li, "Sentiment Analysis of Movie Reviews Based on LSTM-Adaboost," 2022 IEEE 5th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Chongqing, China, 2022, pp. 186-190, doi: 10.1109/IMCEC55388.2022.10019969.

[5]. M. Mishra and A. Patil, "Sentiment Prediction of IMDb Movie Reviews Using CNN-LSTM Approach," 2023 International Conference on Control, Communication and Computing (ICCC), Thiruvananthapuram, India, 2023, pp. 1-6, doi: 10.1109/ICCC57789.2023.10165155.

[6]. S. M. Qaisar, "Sentiment Analysis of IMDb Movie Reviews Using Long Short-Term Memory," 2020 2nd International Conference on Computer and Information Sciences (ICCIS), Sakaka, Saudi Arabia, 2020, pp. 1-4, doi: 10.1109/ICCIS49240.2020.9257657.

[7]. Rehman, A.U., Malik, A.K., Raza, B. et al. A Hybrid CNN-LSTM Model for Improving Accuracy of Movie Reviews Sentiment Analysis. Multimed Tools Appl 78, 26597–26613 (2019). https://doi.org/10.1007/s11042-019-07788-7 .

[8]. Ranjith, V., Barick, R., Pallavi, C.V., Sandesh, S., Raksha, R. (2023). Sentiment Enhanced Smart Movie Recommendation System. In: Shakya, S., Balas, V.E., Haoxiang, W. (eds) Proceedings of Third International Conference on Sustainable Expert Systems . Lecture Notes in Networks and Systems, vol 587. Springer, Singapore. https://doi.org/10.1007/978-981-19-7874-6_4.

[9]. S. Namitha, P. Sanjan, N. C. Reddy, Y. Srikar, H. Shanmugasundaram and B. P. Andraju, "Sentiment Analysis: Current State and Future Research Perspectives," 2023 7th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 2023, pp. 1115-1119, doi: 10.1109/ICICCS56967.2023.10142318.

[10]. Rameshwer Singh, Rajeshwar Singh, Applications of sentiment analysis and machine learning techniques in disease outbreak prediction – A review, Materials Today: Proceedings,Volume 81, Part 2, 2023, Pages 1006-1011, ISSN 2214-7853, https://doi.org/10.1016/j.matpr.2021.04.356. (https://www.sciencedirect.com/science/article/pii/S2214785321032764)

[11]. H. Ge, S. Zheng and Q. Wang, "Based BERT-BiLSTM-ATT Model of Commodity Commentary on The Emotional Tendency Analysis," 2021 IEEE 4th International Conference on Big Data and Artificial Intelligence (BDAI), Qingdao, China, 2021, pp. 130-133, doi: 10.1109/BDAI52447.2021.9515273.

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