Open AccessMulti-Angle Movie Reviews Analysis Based on Multi Model
Author(s) -
Yanzhe Liu,
Bingxiang Liu,
Jiajia Yu,
Ziming Yu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1757/1/012128
Subject(s) - computer science , sentiment analysis , association rule learning , granularity , artificial intelligence , dimensionality reduction , topic model , data mining , tf–idf , text segmentation , machine learning , a priori and a posteriori , natural language processing , information retrieval , segmentation , philosophy , physics , epistemology , quantum mechanics , term (time) , operating system
In this study, movie reviews are used as data sets to extract related phrases, topics, and sentiment scores from the text. Based on users’ information, users’ behavior preferences and their influences are analysed, and text semantic information is mined from multiple perspectives. A variety of data processing and machine learning methods including text segmentation, Apriori association rule mining algorithm, sentiment analysis, linear fitting, TFIDF algorithm, PCA dimensionality reduction, and LDA topic model is used in the research. At the same time, due to the coarse granularity of the topic extraction in the LDA algorithm, it is not suitable for short text, this paper proposes a new topic model based on improved k-means and TextRank and gets good results on this dataset. This paper uses multiple data mining models to analyse film reviews and presents an empirical study of the efficacy of machine learning techniques in text semantic mining.