Sentiment Analysis of Uyghur Text for Fine-grained Opinion Mining
LUO Yawei1, TIAN Shengwei2, YU Long3, Turgun·Ibrahim1, Askar·Hamdulla2
1. School of Information Science and Engineering, Xinjiang University, Urumqi, Xinjiang 830046, China;
2. School of Software, Xinjiang University, Urumqi, Xinjiang 830008, China;
3. Network Center, Xinjiang University, Urumqi, Xinjiang 830046, China
Abstract：Traditional research on sentiment analysis is to determine the sentiment of word, sentence or the whole text, ignoring the topics involved in the sentimental expressions In contrast, this paper proposes a method based on cascade CRFs model to analyze the sentiment at claim level of Uyghur opinioned text. The first layer extracts the topic word and its corresponding opinion word, and determines the scope of opinioned claim, and the result is then passed to the second layer as one of the key features which contributes to sentiment analysis at the claim level. The goal of the sentiment analysis on fine-grained opinion mining is to build a quadruple, which is <opinioned claim, topic word, opinion word, sentiment>. Our experiments show that the precision rate and the recall rate of sentiment analysis reach 77.41% and 78.51%, respectively, demonstrating the efficiency of the proposed method on fine-grained sentiment analysis.
[1] 赵妍妍, 秦兵,刘挺. 文本情感分析[J]. 软件学报, 2010, 21(8): 1834-1848.
[2] 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 (ACL). Philadelphia, USA, 2002: 417-424.
[3] Pang B, Lee L, Vaithyanathan S. Thumbs up? Sentiment Classification using Machine Learning Techniques[C]//Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP). Philadelphia, USA, 2002: 79-86.
[4] Zhao J, Liu K, Wang G. Adding redundant features for CRFs-based sentence sentiment classification[C]//Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing (EMNLP). Honolulu, Hawaii, 2008: 117-126.
[5] 林政, 谭松波, 程学旗. 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.
[6] Lin C H, He Y L. Joint sentiment/topic model for sentiment analysis[C]//Proceeding of the 18th ACM conference on Information and knowledge management. New York, 2009: 375-384.
[7] Jo Y, Oh A. Aspect and sentiment unification mode for online review analysis[C]//Proceedings of the 4th ACM international conference on Web search and data mining. New York, 2011: 815-824.
[8] 孙艳, 周学广, 付伟. 基于主题情感混合模型的无监督文本情感分析[J]. 北京大学学报 (自然科学版), 2013, 49(1): 102-108.
[9] Thet T, Na J, Khoo C. Aspect-based sentiment analysis of movie reviews on discussion boards[J]. Journal of Information Science, 2010, 36(6): 823-848.
[10] 顾正甲, 姚天昉. 评价对象及其倾向性的抽取和判别[J]. 中文信息学报, 2012, 26(4): 91-97.
[11] Tony M, Nigel C. Sentiment analysis using support vector machines with diverse information sources[C]//Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing (EMNLP), Barcelona, Spain, 2004: 412-418.
[12] Jiang L, Yu M, Zhou M, Liu X H, Zhao T J. Target-dependent Twitter Sentiment Classification[C]//Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics(ACL). Portland, Oregon, 2011: 151-160.
[13] 谢丽星, 周明, 孙茂松. 基于层次结构的多策略中文微博情感分析和特征抽取[J]. 中文信息学报, 2012, 26(1): 73-83.
[14] 赵妍研, 秦兵, 车万翔, 等. 基于句法路径的情感评价单元识别[J]. 软件学报, 2011, 22(5): 887-898.
[15] 姚天昉, 程希文, 徐飞玉, 等. 文本意见挖掘综述[J]. 中文信息学报, 2008, 22(3): 71-80.
[16] Kim S-M, Hovy E. Determining the Sentiment of Opinions[C]//Proceedings of the Conference on Computational Linguistics (COLING). Geneva, Switzerland, 2004: 1367-1373.
[17] Liu R, Xiong R, Song L. A Sentiment Classification Method for Chinese Document[C]//Proceedings of the 5th International Conference On Computer Science & Education (ICCSE). Hefei, China, 2010: 918-922.
[18] 易坤秀, 高士杰. 维吾尔语语法[M]. 北京: 中央民族大学出版社, 1998: 10-70.
2016, Vol. 30 
