文本情感倾向性分析

doi:10.3969/j.issn.1006-2475.2017.07.002

摘要/Abstract

摘要：

针对情感分析工作中需要繁琐的人工标注问题，提出一种基于评价单元五元组的情感分析方法。该方法只需合适的情感词典，不需要大量人工标注即可对其进行情感倾向分析。通过联合无监督和有监督学习方法构建评价词表和评价对象词表，在此基础上采用以情感词为链的线性条件随机场模型构建评价单元。最后根据语意搭配关系将评价对象分为4类，情感词分为5类，结合句型、否定词、程度词对情感分析的影响，提出计算文本情感倾向的方法。对比实验表明，本文方法在明显减少人工工作的前提下，取得了较高的F值，并且具有一定的跨领域性。

关键词: 情感分类, 信息抽取, 意见挖掘, 情感分析

Abstract: Aiming at the problem of manual annotation in the text sentiment analysis, a new method based on five tuple of appraisal expression is proposed. This method just needs appropriate sentiment dictionary. The sentiment tendencies of comments are analyzed without lots of markup work. Through the combination of unsupervised and supervised learning methods to construct the evaluation thesaurus and evaluation object list; the extraction of appraisal expression is based on these lists, using linear chain conditional random fields model, which is in the chain of sentiment words. Finally, evaluation objects are divided into four categories and emotional words are divided into five types according to the relationship between semantic collocation, combined with the influence of sentence pattern, negative word and degree word on the sentiment analysis, a method of calculating the sentiment tendency of the text is put forward. Compared with other methods, this method based on the appraisal expression has obtained better F value, and it has a certain cross domain.

Key words: sentiment classification, information extract, opinion mining, sentiment analysis

中图分类号:

TP391

王娜娜，李向前. 文本情感倾向性分析[J]. 计算机与现代化, doi: 10.3969/j.issn.1006-2475.2017.07.002.

WANG Na-na, LI Xiang-qian. Analysis of Text Sentiment Orientation[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2017.07.002.

参考文献

［1］王荣洋，鞠久朋，李寿山. 基于CRFs的评价对象抽取特征研究［J］. 中文信息学报， 2012,26(2):56-61.
［2］朱圣代. 细颗粒度情感分析若干技术研究［D］. 杭州：杭州电子科技大学， 2012.
［3］Hatzivassiloglou V, McKeown K. Predicting the semantic orientation of adjectives［C］// Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and 8th Conference of the European Chapter of the Association for Computational Linguistics. 1997:174-181.
［4］丁晟春，蔡骅. 在线评论信息挖掘研究［M］. 北京：科学出版社, 2014:130-133.
［5］Ding Xiaowen, Liu Bing, Zhang Lei. Entity discovery and assignment for opinion mining applications［C］// Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2009:1125-1134.
［6］Zhao Wayne Xin, Jiang Jing, Yan Hongfei, et al. Jointly modeling aspects and opinions with a MaxEnt-LDA hybrid［C］// Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing. 2010:56-65.
［7］Pang B, Lee L.A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts［C］// Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics. 2004:271-278.
［8］Zhou Guangyou, Zhao Jun, Zheng Daojian, et al. Sentiment classification with graph co-regularization［C］// Proceedings of the 25th International Conference on Computational Linguistics. 2004:1331-1340.
［9］Wilson T, Wiebe J, Hoffmann P. Recognizing contextual polarity in phrase-level sentiment analysis［C］// Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing. 2005:347-354.
［10］Tackstrom O, Mcdonald R. Discovering fine-grained sentiment with latent variable structured prediction models［C］// Proceedings of the 33rd European Conference on Information Retrieval. 2011:368-374.
［11］Tackstrom O, Mcdonald R. Semi-supervised latent variable models for sentence-level sentiment analysis［C］// Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics. 2011:569-574.
［12］Lafferty J D, McCallum A, Pereira F C N. Conditional random fields: Probabilistic models for segmenting and labeling sequence data［C］// Proceedings of the 18th International Conference on Machine Learning. 2001:282-289.
［13］Hu Minqing, Liu Bing. Mining and summarizing customer reviews［C］// Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2004:168-177.
［14］Popescu A-M, Etzioni O. Extracting product features and opinions from reviews［C］// Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing. 2005:339-346.
［15］Toprak C, Jakob N, Gurevych I. Sentence and expression level annotation of opinions in user-generated discourse［C］// Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. 2010:575-584.
［16］Zhuang Li, Jing Feng, Zhu Xiaoyan. Movie review mining and summarization［C］// Proceedings of the 15th ACM International Conference on Information and Knowledge Management. 2006:43-50.
［17］Jakob N, Gurevych I. Extracting opinion targets in a single and cross-domain with conditional random fields［C］// Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2010:1035-1045.

[1]	唐诗琪, 周瑞平, 谢仕斌, 刘梦赤, 肖文, . 基于栈式降噪编码器的跨语言多标签情感分类[J]. 计算机与现代化, 2023, 0(11): 6-12.
[2]	李诗月, 孟佳娜, 于玉海, 李雪莹, 许英傲. 基于知识增强的方面级情感分析方法[J]. 计算机与现代化, 2023, 0(10): 1-8.
[3]	姚春华, 张学磊, 宋馨宇, 张举, 蔡佳志, 冯翱. 一种基于图卷积神经网络和依存分析的财经新闻情感分析方法[J]. 计算机与现代化, 2022, 0(05): 33-39.
[4]	冯茹嘉, 张海军, 潘伟民. 基于情感分析和Transformer模型的微博谣言检测[J]. 计算机与现代化, 2021, 0(10): 1-7.
[5]	尼格拉木·买斯木江, 艾孜尔古丽·玉素甫. 基于BERT及双向GRU模型的慕课用户评论情感倾向性分析[J]. 计算机与现代化, 2021, 0(04): 20-26.
[6]	朱岩, 张利, 王煜. 基于RoBERTa-WWM的中文电子病历命名实体识别[J]. 计算机与现代化, 2021, 0(02): 51-55.
[7]	徐龙. 基于自注意力和胶囊网络的短文本情感分析[J]. 计算机与现代化, 2020, 0(07): 61-64.
[8]	丁岩1,2，鲍焱1,2，胡晓1,2. 基于多媒体信息的双向LSTM情感分析方法[J]. 计算机与现代化, 2019, 0(02): 88-.
[9]	沈然，林恺丰，吴慧. 一种用于供电服务评估的多模态多任务框架[J]. 计算机与现代化, 2018, 0(12): 51-.
[10]	程树东，胡鹰. 基于BI-LSTM-CRF模型的限定领域知识库问答系统[J]. 计算机与现代化, 2018, 0(07): 53-.
[11]	李继东，王移芝. 基于扩展词典与语义规则的中文微博情感分析[J]. 计算机与现代化, 2018, 0(02): 89-.
[12]	刘小明1,2，张英1,2，郑秋生1,2. 基于卷积神经网络模型的互联网短文本情感分类[J]. 计算机与现代化, 2017, 0(4): 73-77.
[13]	伊尔夏提·吐尔贡,吾守尔·斯拉木,热西旦木·吐尔洪太,于清. 维吾尔文情感语料库的构建与分析[J]. 计算机与现代化, 2017, 0(4): 67-72.
[14]	石强强1，赵应丁2,3，杨红云2,3. 基于SVM的酒店客户评论情感分析[J]. 计算机与现代化, 2017, 0(3): 117-.
[15]	孙梦，瞿有利. 一种基于噪音过滤包装器的Web新闻抽取方法[J]. 计算机与现代化, 2017, 0(1): 5-12.