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数据分析与知识发现  2021, Vol. 5 Issue (4): 72-79     https://doi.org/10.11925/infotech.2096-3467.2020.1083
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于生成式对抗网络和评论专业类型的情感分类研究 *
李菲菲1,吴璠2,王中卿2()
1苏州大学图书馆 苏州 215006
2苏州大学计算机科学与技术学院 苏州 215006
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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摘要 

【目的】 研究评论文本中专业评论家和普通观众表达情感的方式的差异,提高评论情感分类的准确率。【方法】 利用用户的专业类型辅助判断评论的情感极性,使用生成式对抗网络分析评论来自专业评论家还是普通观众,通过捕获两者在表达情感方式上的差异性,进一步提高评论情感分类的准确度。【结果】 实验证明,提出的基于生成式对抗网络和评论专业类型的情感分类模型GJOINT准确率达到0.836,比基准模型LSTM、BiLSTM分别提高了5.6%、4.4%。【局限】 实验数据集只选取电影评论数据集,在其他领域数据集上的有效性需要进一步验证。【结论】 提出的基于生成式对抗网络和评论专业类型的情感分类模型GJOINT能有效提高在线评论情感分类的效果。

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李菲菲
吴璠
王中卿
关键词 情感分类生成式对抗网络评论专业类型LSTM    
Abstract

[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
收稿日期: 2020-11-02      出版日期: 2021-05-17
ZTFLH:  分类号: TP391  
基金资助:*国家自然科学基金项目的研究成果之一(61806137);国家自然科学基金项目的研究成果之一(61702351)
通讯作者: 王中卿     E-mail: wangzq@suda.edu.cn
引用本文:   
李菲菲,吴璠,王中卿. 基于生成式对抗网络和评论专业类型的情感分类研究 *[J]. 数据分析与知识发现, 2021, 5(4): 72-79.
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.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2020.1083      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2021/V5/I4/72
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。)
Table 1  评论实例
Fig.1  情感分类和评论专业类型分类联合模型
Fig.2  基于生成式对抗网络和评论专业类型的情感分类模型
实验模型 实验一准确率 实验二准确率
A C A C
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
Table 2  专业类型不同的评论情感分类结果
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
Table 3  情感分类性能对比
评论内容 LSTM GJOINT
[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.
(从今天的标准来看,它最有勇气的一点是结束时没有一个必然的快乐结局,一个观看了187分钟的观众不会得到一个整洁、愚蠢的结论。)
Negative Positive
[E4] boring old hollywood style epic with that canned elevator music. still it was the best of its kind.
(无聊的老好莱坞风格史诗与罐装电梯音乐。尽管如此,它还是同类型中最好的。)
Negative Positive
Table 4  GJOINT模型有效性评估实例
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