摘要
由于Web文本迅速增多,对这些文本,特别是用户主动发布的评论数据进行挖掘和分析,识别出其情感趋向及演化规律,可以更好地理解用户的消费习惯,分析热点舆情,给企业、政府等机构提供重要的决策依据。首先对情感分析的研究对象和目标进行了定义和说明,并给出基本的研究思路。然后,在主观性句子识别任务上,详细回顾和分析了主要的处理方法;在观点分类的特征抽取上,重点介绍和讨论了两类主流的处理思路——基于情感词和基于频繁模式挖掘。接着简要介绍了其他一些相关的情感分析问题。最后总结了情感分析的现有成就和不足,以及面临的挑战,并对其发展前景进行了展望。
With the rapid growth of the Web text data, mining and analyzing these text data, especially the online review data posted by the users, can greatly help better understand the users＇ consuming habits and public opinions, and plays an important role in decision-making for the enterprises and the government. This survey first introduces the motivation, research problems and goals of sentiment analysis, and presents some basic technologies used in sentiment analysis. It then describes one of the major tasks in sentiment analysis, subjective sentence detection, by reviewing and analyzing some recent work in this area. Next, it focuses on another important task in sentiment analysis, opinion classification, and discusses two leading feature extraction techniques for opinion classification, sentimental word based and frequent pattern based methods. Furthermore, it also introduces several other relevant sentiment analysis problems. Finally, the paper summarizes the current status, remaining challenges, and future directions in the field of sentiment analysis.
出处
《计算机应用》
CSCD
北大核心
2008年第11期2725-2728,共4页
journal of Computer Applications
关键词
情感分析
综述
观点分类
主观性识别
特征抽取
sentiment analysis
survey
opinion classification
subjectivity detection
feature extraction
