Text sentiment analysis of film reviews using Word2Vec-LSTM

Linghui Li; Zhongliang Guan; Fuchun Xing

doi:10.1117/12.3031046

19 July 2024 Text sentiment analysis of film reviews using Word2Vec-LSTM

Linghui Li, Zhongliang Guan, Fuchun Xing

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131810T (2024) https://doi.org/10.1117/12.3031046
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

With the continuous development of the film industry, audience expectations and demands are increasing. Film reviews, as crucial sources of feedback, play a significant role in the film market. Through data mining of film review data, valuable insights can be obtained regarding audience evaluations and preferences, facilitating informed decision-making in the film industry. This study focuses on analyzing film review data obtained through Scrapy technology from the TOP250 Douban movies. The Word2Vec and LSTM is employed to achieve sentiment classification of movie reviews. To evaluate the model's classification ability, a comparative analysis is conducted with several mainstream machine learning models. Experimental results demonstrate that the Word2Vec-LSTM model exhibits superior classification performance, providing film industry practitioners and researchers with valuable insights into audience perceptions of movies. This study contributes to a better understanding of audience emotions and offers meaningful contributions to the film industry.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Linghui Li, Zhongliang Guan, and Fuchun Xing "Text sentiment analysis of film reviews using Word2Vec-LSTM", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131810T (19 July 2024); https://doi.org/10.1117/12.3031046

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