Open AccessSentiment analysis of movie reviews based on deep learning
Author(s) -
Fuqian Zhang,
Qingtao Zeng,
Likun Lu,
Yeli Li
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 - 10.1088/1742-6596/1754/1/012234
Subject(s) - sentiment analysis , the internet , artificial intelligence , computer science , deep learning , public opinion , natural language processing , word (group theory) , field (mathematics) , scalability , support vector machine , data science , machine learning , world wide web , linguistics , political science , philosophy , mathematics , politics , pure mathematics , law , database
In recent years, with the rapid development of NLP (Natural Language Processing) and deep learning, public opinion and public opinion on the Internet have decreased a lot compared to the past. Many Internet users have changed from mere "bystanders" to disseminators of Internet information. Movie review sentiment analysis technology is an emerging category in the field of information mining. More and more people have joined the "review army". The quality of a movie is closely related to movie reviews! The manual screening method not only consumes a lot of manpower and material resources, but also is inefficient. Therefore, the use of deep learning-based sentiment analysis has become the current general trend. Based on the principle of word mosaic (word vector) and deep learning, this paper proposes a movie review sentiment analysis technology based on deep learning and machine learning word mosaic. Experiments show that the method used in this article has reached the correct rate of emotional classification of movie reviews. 83.13%, the experimental results prove the practicability and scalability of this method and the effectiveness of this method.