卢奇,陈文亮. 大规模中文实体情感知识的自动获取[J]. 中文信息学报, 2018, 32(8): 32-41.
LU Qi, CHEN Wenliang. Automatically Building a Large Scale Dictionary of Chinese Entity Sentiment Expressions. , 2018, 32(8): 32-41.
Automatically Building a Large Scale Dictionary of Chinese Entity Sentiment Expressions
LU Qi1,2, CHEN Wenliang1,2
1.School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China; 2.Collaborative Innovation Center of Novel Software Technology and Industrialization, Suzhou, Jiangsu 215006, China
Abstract：Except for some sentiment dictionaries. There are not sentiment expressions for entities which are very important for analysis. This paper proposes a method of automatically building a dictionary of entity sentiment expressions from large-scale raw text. In our method, we use a sorting algorithm based on a bipartite graph to rank the candidates of sentiment expressions. Then, we present a refining algorithm according to semantic similarity to extract some expressions from the low-rank set. Finally, we conduct the experiments on three datasets from different domains. The experimental results show that the accuracy of the extracted expressions is better than 90%. Totally we obtain a large scale dictionary including about 300K sentiment expressions.
[1] Pang B, Lee L. Opinion mining and sentiment analysis[J]. Foundations and Trends in Information Retrieval, 2008, 2(1-2): 1-135. [2] Pang B, Lee L, Vaithyanathan S. Thumbs up? Sentiment classification using machine learning techniques[C]//Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing-Volume 10. Association for Computational Linguistics, 2002: 79-86. [3] 宗成庆. 统计自然语言处理[M]. 北京: 清华大学出版社, 2008: 1-475. [4] Ku L W, Chen H H. Mining opinions from the Web: Beyond relevance retrieval[J]. Journal of the American Society for Information Science and Technology, 2007, 58(12): 1838-1850. [5] Li J, Sun M. Experimental study on sentiment classification of Chinese review using machine learning techniques[C]//Proceedings of International Conference on Natural Language Processing and Knowledge Engineering, 2007. Nlp-Ke. 2007: 1-12. [6] Xu G, Meng X, Wang H. Build Chinese emotion lexicons using a graph-based algorithm and multiple resources.[C]//Proceedings of COLING 2010, International Conference on Computational Linguistics, Proceedings of the Conference, 23-27 August 2010, Beijing, China. 2010: 1209-1217. [7] 刘知远, 崔安颀. 大数据智能[J]. 信息安全与通信保密, 2016, 2: 066. [8] Esuli A, Sebastiani F. SentiWordNet: A publicly available lexical resource for opinion mining[C]//Proceedings of LREC, 2006, 6: 417-422. [9] Baccianella S, Esuli A, Sebastiani F. SentiWordNet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining[C]//Proceedings of LREC, 2010, 10: 2200-2204. [10] Turney P D. Thumbs up or thumbs down?: Semantic orientation applied to unsupervised classification of reviews[C]//Proceedings of the 40th Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics, 2002: 417-424. [11] Banea C, Wiebe J M, Mihalcea R. A bootstrapping method for building subjectivity lexicons for languages with scarce resources[C]//Proceedings of International Conference on Language Resources and Evaluation, Lrec 2008. DBLP, 2009: 2764-2767. [12] Hatzivassiloglou V, McKeown K R. Predicting the semantic orientation of adjectives[C]//Proceedings of the Eighth Conference on European Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, 1997: 174-181. [13] Kanayama H, Nasukawa T. Fully automatic lexicon expansion for domainoriented sentiment analysis[C]//Proceedings of Conference on Empirical Methods in Natural Language Processing, 2006: 355-363. [14] K Qiu G, Liu B, Bu J, et al. Expanding domain sentiment lexicon through double propagation[C]//Proceedings of International Jont Conference on Artifical Intelligence. Morgan Kaufmann Publishers Inc. 2009: 1199-1204. [15] Zhang L, Liu B, Lim S H, et al. Extracting and ranking product features in opinion documents[C]//Proceedings of International Conference on Computational Linguistics: Posters. Association for Computational Linguistics, 2010: 1462-1470. [16] K Qiu G, Liu B, Bu J, et al. Opinion word expansion and target extraction through double propagation[J]. Computational Linguistics, 2011, 37(1): 9-27. [17] Agathangelou P, Katakis I, Kokkoras F, et al. Mining domain-specific dictionaries of opinion words[C]//Proceedings of Web Information System Engineering, 2014: 47-62. [18] 李智超.面向互联网评论的情感资源构建及应用研究[D].北京: 清华大学博士学位论文,2011. [19] Popescu A M, Etzioni O. Extracting product features and opinions from reviews[M]. Natural Language Processing and Text Mining. Springer London, 2007: 9-28. [20] 刘鸿宇, 赵妍妍, 秦兵, 等. 评价对象抽取及其倾向性分析[J]. 中文信息学报, 2010, 24(1): 84-88. [21] Zhuang L, Jing F, Zhu X Y. Movie review mining and summarization[C]//Proceedings of the 15th ACM International Conference on Information and Knowledge Management. ACM, 2006: 43-50. [22] Kobayashi N, Inui K, Matsumoto Y, et al. Collecting evaluative expressions for opinion extraction[C]//Proceedings of International Conference on Natural Language Processing. Berlin Heidelberg: Springer, 2004: 596-605. [23] Somprasertsri G, Lalitrojwong P. Mining feature-opinion in online customer reviews for opinion summarization[J]. J. UCS, 2010, 16(6): 938-955. [24] 王素格, 吴苏红. 基于依存关系的旅游景点评论的特征-观点对抽取[J]. 中文信息学报, 2012, 26(3): 116-122. [25] Page L, Brin S, Motwani R, et al. The PageRank citation ranking: Bringing order to the web[J]. Stanford Digital Libraries Working Paper, 1999: 9(1): 1-14. [26] Zhang R, Zettsu K, Kidawara Y, et al. Context-sensitive web service discovery over the bipartite graph model[J]. Frontiers of Computer Science, 2013, 7(6): 875-893. [27] Mikolov T, Chen K, Corrado G, et al. Efficient estimation of word representations in vector space[J]. arXiv preprint arXiv: 1301.3781, 2013.
2018, Vol. 32 
