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Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon

机译：基于主题建模和HowNet词典的中文在线社会评论多方面情感分析

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摘要

User-generated reviews on the Web reflect users' sentiment about products, services and social events. Existing researches mostly focus on the sentiment classification of the product and service reviews in document level. Reviews of social events such as economic and political activities, which are called social reviews, have specific characteristics different to the reviews of products and services. In this paper, we propose an unsupervised approach to automatically discover the aspects discussed in Chinese social reviews and also the sentiments expressed in different aspects. The approach is called Multi-aspect Sentiment Analysis for Chinese Online Social Reviews (MSA-COSRs). We first apply the Latent Dirichlet Allocation (LDA) model to discover multi-aspect global topics of social reviews, and then extract the local topic and associated sentiment based on a sliding window context over the review text. The aspect of the local topic is identified by a trained LDA model, and the polarity of the associated sentiment is classified by HowNet lexicon. The experiment results show that MSA-COSR cannot only obtain good topic partitioning results, but also help to improve sentiment analysis accuracy. It helps to simultaneously discover multi-aspect fine-grained topics and associated sentiment.
机译：用户在Web上生成的评论反映了用户对产品，服务和社交活动的看法。现有研究主要集中在文档级别的产品和服务评论的情感分类上。诸如经济和政治活动之类的社会事件的评论（称为社会评论）具有与产品和服务的评论不同的特定特征。在本文中，我们提出了一种无监督的方法来自动发现中国社会评论中讨论的方面以及在不同方面表达的情感。该方法称为中文在线社会评论的多方面情感分析（MSA-COSR）。我们首先应用潜在Dirichlet分配（LDA）模型来发现社会评论的多方面全局主题，然后基于评论文本上的滑动窗口上下文提取本地主题和相关的情感。本地主题的方面是由经过训练的LDA模型标识的，而相关情感的极性则由HowNet词典进行分类。实验结果表明，MSA-COSR不仅可以获得良好的主题划分结果，而且还有助于提高情感分析的准确性。它有助于同时发现多方面的细粒度主题和相关情绪。

著录项

  • 来源
    《Knowledge-Based Systems》 |2013年第1期|186-195|共10页
  • 作者

    Fu Xianghua; Liu Guo; Guo Yanyan; Wang Zhiqiang;

  • 作者单位

    College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China;

    College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China;

    College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China;

    College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    aspect detection; sentiment analysis; social reviews; topic modeling; hownet lexicon;

    机译：方面检测;情绪分析;社会评论;主题建模;知网词典;

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