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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (4): 72-79    DOI: 10.11925/infotech.2096-3467.2020.1083
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Sentiment Analysis with Reviewer Types and Generative Adversarial Network
Li Feifei1,Wu Fan2,Wang Zhongqing2()
1Library of Soochow University, Suzhou 215006, China
2School of Computer Science and Technology, Soochow University, Suzhou 215006, China
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[Objective] This paper analyzes online comments by professional critics and average audience, aiming to improve the sentiment classification of reviews. [Methods] First, we introduced the professional backgrounds of contributors to examine the emotional polarity of reviews. Then, we used the generative adversarial network to decide whether the contributor was a professional critic or an average browser. Finally, we identified their differences to further improve the accuracy of emotion classification. [Results] The accuracy rate of the proposed model reached 83.6%, which was 5.6% higher than the benchmark model LSTM and 4.4% higher than BiLSTM. [Limitations] We only studied movie reviews, and more research is needed to evaluate our model with data sets from other fields. [Conclusions] The proposed GJOINT model can effectively improve the results of sentiment classification of online reviews.

Key wordsSentiment Analysis      Generative Adversarial Network      Review Professionalism      LSTM     
Received: 02 November 2020      Published: 17 May 2021
ZTFLH:  分类号: TP391  
Fund:National Natural Science Foundation of China(61806137);National Natural Science Foundation of China(61702351)
Corresponding Authors: Wang Zhongqing     E-mail:

Cite this article:

Li Feifei,Wu Fan,Wang Zhongqing. Sentiment Analysis with Reviewer Types and Generative Adversarial Network. Data Analysis and Knowledge Discovery, 2021, 5(4): 72-79.

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序号 评论实例
[E1] Funny and charming, it’s a very good movie. (既有趣又迷人,是一部很好的电影。)
[E2] This version of Black Panther weaves a seemingly disparate set of strands together rather seamlessly, presenting more than enough comic book acrobatics and mayhem to satisfy the core fan base while also speaking a necessary degree of truth to power. (这个版本的黑豹把一组看似不同的线编织在一起,展现了足够多的漫画书,杂技和混乱,以满足核心粉丝群,同时也展示了必要的真实性。)
Comment examples
The JOINT Model
The GJOINT Model
实验模型 实验一准确率 实验二准确率
SingleCategory 0.678 0.734 0.768 0.802
CrossCategory 0.648 0.623 0.746 0.706
MultiCategory 0.712 0.685 0.782 0.816
The Results of Review Sentiment Analysis with Different Professional Type
模型名称 实验一准确率 实验二准确率
LSTM 0.687 0.780
BiLSTM 0.672 0.792
JOINT 0.703 0.806
GJOINT 0.716 0.836
Performance Comparison of Sentiment Analysis
[E3] The most courageous thing about it, from today’s standards, is that it closes without an obligatory happy ending, and an audience that has watched for 187 minutes doesn’t get a tidy, mindless conclusion.
Negative Positive
[E4] boring old hollywood style epic with that canned elevator music. still it was the best of its kind.
Negative Positive
Examples of Effectiveness Assessment by GJOINT
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