Sentiment analysis of movie reviews based on logistic regression model

Yichen Xiao; Guanwen Yan; Yushi Yan; Yuhao Xiang

doi:10.1117/12.3038109

7 August 2024 Sentiment analysis of movie reviews based on logistic regression model

Yichen Xiao, Guanwen Yan, Yushi Yan, Yuhao Xiang

Proceedings Volume 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024); 1322933 (2024) https://doi.org/10.1117/12.3038109
Event: Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 2024, Nanchang, China

Abstract

Movie reviews can help viewers understand basic information about a movie in advance, and film ratings play a vital role in the audience's choice of which movie to watch. Due to the large number of comments left by viewers on movie ticketing websites, it is difficult for viewers to distinguish between good and bad movie reviews and cannot judge the quality of the movie. Therefore, it is necessary to perform sentiment analysis on movie reviews. This paper uses 104,215 movie reviews collected from web pages as experimental subjects, uses TF-IDF to extract text features, and performs sentiment analysis on each movie review through logistic regression algorithm and naive Bayes algorithm to determine the emotional state represented in it. Positive or negative. The results of the two algorithms were compared based on 5 evaluation metrics (accuracy, precision, recall, F1 measure, and area under the curve (AUC)). Experimental results show that logistic regression is better at predicting movie reviews than naive Bayes.

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

Citation Download Citation

Yichen Xiao, Guanwen Yan, Yushi Yan, and Yuhao Xiang "Sentiment analysis of movie reviews based on logistic regression model", Proc. SPIE 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 1322933 (7 August 2024); https://doi.org/10.1117/12.3038109

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KEYWORDS

Data modeling

Analytical research

Feature extraction

Machine learning

Performance modeling

Emotion

Matrices

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