《计算机应用研究》|Application Research of Computers

基于关键句分析的微博情感倾向性研究

Microblog sentiment orientation analysis based on key sentence analysis

免费全文下载 (已被下载 次)  
获取PDF全文
作者 邵帅,刘学军,李斌
机构 南京工业大学 计算机科学与技术学院,南京 211816
统计 摘要被查看 次,已被下载
文章编号 1001-3695(2018)04-0982-06
DOI 10.3969/j.issn.1001-3695.2018.04.005
摘要 针对微博的情感倾向分析,提出了一种基于关键句分析的微博情感倾向性分析方法SOAS(sentiment orientation analysis based on key sentence analysis),实现了从句子级到文档级的情感分析。首先,利用关键句抽取算法得到微博关键句,关键句抽取主要考虑位置属性、关键词属性和词频句子频特征这三类属性;之后,结合依存句法分析提出了影响情感倾向的七种词性搭配,并针对这七种搭配给出了六种情感计算规则,计算关键句的情感倾向值;最后,以关键句得分为权重,对所有关键句的情感倾向值加权求和得到微博的情感倾向。实现结果表明,基于关键句分析的微博情感倾向算法的情感分析,比同类算法的准确率高出了10.55%,提高了情感分析的准确率,具有高效性。
关键词 情感分析;倾向性分析;关键句;依存句法分析;观点挖掘
基金项目 国家自然科学基金资助项目(61203072)
江苏省重点研发计划资助项目(BE2015697)
本文URL http://www.arocmag.com/article/01-2018-04-005.html
英文标题 Microblog sentiment orientation analysis based on key sentence analysis
作者英文名 Shao Shuai, Liu Xuejun, Li Bin
机构英文名 SchoolofComputerScience&Technology,NanjingUniversityofTechnology,Nanjing211816,China
英文摘要 This paper put forward a microblog sentiment orientation method SOAS based on the analysis of special sentences, it achieved sentiment analysis from sentence level to the document level. Firstly, it extracted the key sentences of microblog sentiment by key sentence extraction algorithm. This algorithm mainly considered 3 characteristics, namely location, keyword and the frequency characteristic of word and sentence frequency. Then, it computed the value of key sentences’ sentiment orientation by 6 emotion calculation rules. These rules were given by 7 kinds of collocation of part of speech which would affect sentiment orientation. The 7 kinds of collocation of part of speech were extracted by dependency parsing. Finally, it used the value of key sentences as weights to weight sum all key sentences to judge sentiment orientation. The experiments demonstrate that the SOAS improves the accuracy of sentiment analysis. Compared to similar algorithms, its accuracy is 10.55% higher than that of others. It improves accuracy while having high efficiency.
英文关键词 sentiment analysis; orientation analysis; key sentence; dependence parsing; opinion mining
参考文献 查看稿件参考文献
  [1] 你们关注疫苗案的时间有多久?三秒?[EB/OL] . (2016-04-15). http://money. 163. com/16/0415/14/BKMT21IV00253B0H. html.
[2] Taboada M, Brooke J, Tofiloski M, et al. Lexicon-based methods for sentiment analysis[J] . Computational Linguistics, 2011, 37(2):267-307.
[3] Paltoglou G, Thelwall M. A study of information retrieval weighting schemes for sentiment analysis[C] //Proc of the 48th Annual Meeting of the Association for Computational Linguistics. Stroudsburg:ACL, 2010:1386-1395.
[4] 林政, 谭松波, 陈学旗. 基于情感关键句抽取的情感分类研究[J] . 计算机研究与发展, 2012, 49(11):2376-2382.
[5] 冯时, 付永陈, 杨峰, 等. 基于依存句法的博文情感倾向分析研究[J] . 计算机研究与发展, 2012, 49(11):2395-2406.
[6] 万常选, 江腾蛟, 钟敏娟, 等. 基于词性标注和依存句法的Web金融信息情感计算[J] . 计算机研究与发展, 2013, 50(12):2554-2569.
[7] Zhao Yanyan, Qin Bin, Che Wanxiang, et al. Appraisal expression recongnition with syntactic path for sentence sentiment classification[J] . International Journal of Computer Processing of Languages, 2011, 23(1):23-37.
[8] Wu Yanyan, Zhang Qi, HuangXuanjing, et al. Phrase dependency parsing for opinon mining[C] //Proc of the 47th Annual Meeting of the Association for Computational Linguistics. Stroudsburg:ACL, 2009:1533-1541.
[9] 江腾蛟, 万常选, 刘德喜, 等, 基于语义分析的评价对象—情感词对抽取[J] . 计算机学报, 2016, 39(3):1-17.
[10] 刘海涛. 依存句法的理论与实践[M] . 北京:科学出版社, 2009.
[11] 姚天昉, 娄德成. 汉语语句主题语义倾向性分析方法的研究[J] . 中文信息学报, 2007, 21(5):73-79.
[12] 微博情感分析数据集[DB/OL] . (2015-10-14). http://download. csdn. net/download/turkan/9181661.
收稿日期 2016/12/22
修回日期 2017/2/23
页码 982-987
中图分类号 TP391
文献标志码 A