Research Article
Sentiment Analysis of Film Reviews Based on Deep Learning Model Collaborated with Content Credibility Filtering
@INPROCEEDINGS{10.1007/978-3-030-67537-0_19, author={Xindong You and Xueqiang Lv and Shangqian Zhang and Dawei Sun and Shang Gao}, title={Sentiment Analysis of Film Reviews Based on Deep Learning Model Collaborated with Content Credibility Filtering}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 16th EAI International Conference, CollaborateCom 2020, Shanghai, China, October 16--18, 2020, Proceedings, Part I}, proceedings_a={COLLABORATECOM}, year={2021}, month={1}, keywords={Sentiment analysis Film reviews mining Natural language processing Deep learning Credibility algorithm}, doi={10.1007/978-3-030-67537-0_19} }- Xindong You
Xueqiang Lv
Shangqian Zhang
Dawei Sun
Shang Gao
Year: 2021
Sentiment Analysis of Film Reviews Based on Deep Learning Model Collaborated with Content Credibility Filtering
COLLABORATECOM
Springer
DOI: 10.1007/978-3-030-67537-0_19
Abstract
Sentiment analysis of film reviews is the basis of obtaining the opinions of movie viewers. It has an important influence on movie public opinion control and stimulating potential viewers. Due to the natural openness and randomness of social media, there may exist a considerable amount of useless or false information in film review comments, making it challenging to analyze the credibility of the comments. This paper proposes a fine-grained sentiment analysis method based on the key-viewpoint sentences of Chinese film reviews, where a deep learning model is used to classify the fine-grained emotions in film reviews. Based on the analysis results, a method for calculating the credibility of review comments is proposed. Under the credibility criteria, corpus screened through credibility filtering algorithm, the overall sentiment classification can obtain 9% improvement on accuracy than the original corpus, which verifies the validity of the credibility algorithm. The higher quality corpus achieved by the credibility algorithm is benefit for improving the accuracy of the sentiment classification.
