ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2017, Vol. 54 ›› Issue (6): 1150-1170.doi: 10.7544/issn1000-1239.2017.20160807

所属专题: 2017优青专题

• 综述 • 上一篇    下一篇

情感分类研究进展

陈龙,管子玉,何金红,彭进业   

  1. (西北大学信息科学与技术学院 西安 710127) (longchen@stumail.nwu.edu.cn)
  • 出版日期: 2017-06-01
  • 基金资助: 
    国家自然科学基金优秀青年科学基金项目(61522206)

A Survey on Sentiment Classification

Chen Long, Guan Ziyu, He Jinhong, Peng Jinye   

  1. (School of Information Science and Technology, Northwest University, Xi’an 710127)
  • Online: 2017-06-01

摘要: 文本情感分析是多媒体智能理解的重要问题之一.情感分类是情感分析领域的核心问题,旨在解决评论情感极性的自动判断问题.由于互联网评论数据规模与日俱增,传统基于词典的方法和基于机器学习的方法已经不能很好地处理海量评论的情感分类问题.随着近年来深度学习技术的快速发展,其在大规模文本数据的智能理解上表现出了独特的优势,越来越多的研究人员青睐于使用深度学习技术来解决文本分类问题.主要分为2个部分:1)归纳总结传统情感分类技术,包括基于字典的方法、基于机器学习的方法、两者混合方法、基于弱标注信息的方法以及基于深度学习的方法;2)针对前人情感分类方法的不足,详细介绍所提出的面向情感分类问题的弱监督深度学习框架.此外,还介绍了评论主题提取相关的经典工作.最后,总结了情感分类问题的难点和挑战,并对未来的研究工作进行了展望.

关键词: 情感分析, 情感分类, 深度学习, 弱监督, 主题提取

Abstract: Sentiment analysis in text is an important research field for intelligent multimedia understanding. The aim of sentiment classification is to predict the sentiment polarity of opinionated text, which is the core of sentiment analysis. With rapid growth of online opinionated content, the traditional approaches such as lexicon-based methods and classic machine learning methods cannot well handle large-scale sentiment classification problems. In recent years, deep learning has achieved good performance on the intelligent understanding of large-scale text data and has attracted a lot of attention. More and more researchers start to address text classification problems with deep learning. The content of this survey is organized as two parts. We firstly summarize the traditional approaches including lexicon-based methods, machine learning based methods, hybrid methods, methods based on weakly labeled data and deep learning based methods. Secondly, we introduce our proposed weakly-supervised deep learning framework to deal with the defects of the previous approaches. Moreover, we briefly summarize the research work on the extraction of opinion aspects. Finally, we discuss the challenges and future work on sentiment classification.

Key words: sentiment analysis, sentiment classification, deep learning, weak-supervision, aspects extraction

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