Razia Sulthana, A., Jaithunbi, A.K., Harikrishnan Haritha Varadarajan Vijayakumar. (2022) Sentiment Analysis on Movie Reviews Dataset Using Support Vector Machines and Ensemble Learning. International Journal of Information Technology and Web Engineering (IJITWE), 17, 1-23.
https://doi.org/10.4018/IJITWE.311428

has been cited by the following article:

• TITLE: Film and Television Website Scores Authenticity Verification Based on the Emotional Analysis

  AUTHORS: Weiyu Tong

  KEYWORDS: Bi-LSTM Model, Film Review Emotion Analysis, Naive Bayes, Python, Data Crawl

  JOURNAL NAME: Journal of Computer and Communications, Vol.12 No.2, February 29, 2024

  ABSTRACT: Sentiment analysis is a method to identify and understand the emotion in the text through NLP and text analysis. In the era of information technology, there is often a certain error between the comments on the movie website and the actual score of the movie, and sentiment analysis technology provides a new way to solve this problem. In this paper, Python is used to obtain the movie review data from the Douban platform, and the model is constructed and trained by using naive Bayes and Bi-LSTM. According to the index, a better Bi-LSTM model is selected to classify the emotion of users’ movie reviews, and the classification results are scored according to the classification results, and compared with the real ratings on the website. According to the error of the final comparison results, the feasibility of this technology in the scoring direction of film reviews is being verified. By applying this technology, the phenomenon of film rating distortion in the information age can be prevented and the rights and interests of film and television works can be safeguarded.