Wang Yufei
School of Film and Television Media, Wuchang University of Technology, Wuhan, Hubei, China.
PeerJ Comput Sci. 2023 Apr 3;9:e1295. doi: 10.7717/peerj-cs.1295. eCollection 2023.
In recent years, with the popularity of the Internet, more and more people like to comment on movies they have watched on the film platform after watching them. These reviews hide the reviewers' feedback on films. Mining the emotional orientation information in these reviews can provide consumers with shopping references and help businesses optimize film works and improve business strategies. Therefore, the emotional classification of film reviews has high research value because few emotion dictionaries and analysis tools are available for reference and use in film reviews. The accuracy of emotion classification still needs to be improved. This study introduces the attention mechanism and dual channel long short term memory (DC-LSTM) while building the emotion dictionary in the field of Chinese film review. It classifies Chinese film reviews in terms of topic-based fine-grained emotion. First, the emotion vector is constructed using the constructed movie review emotion lexicon. The semantic vector obtained by the Word2vector tool is input to LSTM to encode the comment text. Then, the topic attention module is used to decode. Finally, the final emotion classification result is obtained through the softmax function of the entire link layer and the output layer. The thematic attention modules constructed in this study are independent of each other for attention parameter adjustment and learning. One attention module corresponds to one film theme. In this study, eight themes, including "plot," "special effects," "original work," "music," "thought," "theme," "acting skills," and "joke," were extracted, and each theme was classified into three types of emotions: "positive," "neutral," and "negative." The experimental results on the crawled Chinese film review dataset show that the proposed algorithm is superior to some existing algorithms and models in accuracy, precision, recall and F1 measure. The DCLSTM based on the thematic attention mechanism (DCLSTM-TAM) model constructed in this study introduces the emotion vector into the network and adds the theme attention mechanism. It can not only classify the emotion for different topics of a film review but also effectively deal with film reviews with fuzzy emotional tendencies. It realizes the fine-grained emotion classification of film topics and improves the accuracy of emotion classification of film reviews. The emotion classification method and model proposed in this study have good transferability, and the change of training corpus is also applicable to other short text fields.
近年来，随着互联网的普及，越来越多的人喜欢在观看电影后在电影平台上对所观看的电影进行评论。这些评论隐藏着评论者对电影的反馈。挖掘这些评论中的情感倾向信息，可以为消费者提供购物参考，帮助企业优化电影作品、改进经营策略。因此，电影评论的情感分类具有较高的研究价值，因为可供电影评论参考使用的情感词典和分析工具较少，情感分类的准确率仍有待提高。本研究在构建中文电影评论领域情感词典的同时引入注意力机制和双通道长短期记忆网络（DC-LSTM），对中文电影评论进行基于主题的细粒度情感分类。首先，利用构建的电影评论情感词典构建情感向量，将Word2vector工具得到的语义向量输入LSTM对评论文本进行编码，然后利用主题注意力模块进行解码，最后通过全连接层和输出层的softmax函数得到最终的情感分类结果。本研究构建的主题注意力模块相互独立，用于注意力参数的调整和学习，一个注意力模块对应一个电影主题。本研究提取了“情节”“特效”“原著”“音乐”“思想”“主题”“演技”“笑点”8个主题，每个主题分为“积极”“中性”“消极”3种情感类型。在爬取的中文电影评论数据集上的实验结果表明，所提算法在准确率、精确率、召回率和F1值方面优于一些现有算法和模型。本研究构建的基于主题注意力机制的DCLSTM（DCLSTM-TAM）模型将情感向量引入网络并添加了主题注意力机制，不仅能够对电影评论的不同主题进行情感分类，还能有效处理情感倾向模糊的电影评论，实现了电影主题的细粒度情感分类，提高了电影评论情感分类的准确率。本研究提出的情感分类方法和模型具有良好的可迁移性，训练语料库的变化也适用于其他短文本领域。
