acl acl2011 acl2011-292 acl2011-292-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Long Jiang ; Mo Yu ; Ming Zhou ; Xiaohua Liu ; Tiejun Zhao
Abstract: Sentiment analysis on Twitter data has attracted much attention recently. In this paper, we focus on target-dependent Twitter sentiment classification; namely, given a query, we classify the sentiments of the tweets as positive, negative or neutral according to whether they contain positive, negative or neutral sentiments about that query. Here the query serves as the target of the sentiments. The state-ofthe-art approaches for solving this problem always adopt the target-independent strategy, which may assign irrelevant sentiments to the given target. Moreover, the state-of-the-art approaches only take the tweet to be classified into consideration when classifying the sentiment; they ignore its context (i.e., related tweets). However, because tweets are usually short and more ambiguous, sometimes it is not enough to consider only the current tweet for sentiment classification. In this paper, we propose to improve target-dependent Twitter sentiment classification by 1) incorporating target-dependent features; and 2) taking related tweets into consideration. According to the experimental results, our approach greatly improves the performance of target-dependent sentiment classification. 1
Ralitsa Angelova, Gerhard Weikum. 2006. Graph-based text classification: learn from your neighbors. SIGIR 2006: 485-492 Luciano Barbosa and Junlan Feng. 2010. Robust Sentiment Detection on Twitter from Biased and Noisy Data. Coling 2010. Christopher Burges. 1998. A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery, 2(2): 121-167. Yejin Choi, Claire Cardie, Ellen Riloff, and Siddharth Patwardhan S. 2005. Identifying sources of opinions with conditional random fields and extraction patterns. In Proc. of the 2005 Human Language Technology Conf. and Conf. on Empirical Methods in Natural Language Processing (HLT/EMNLP 2005). pp. 355-362 Dmitry Davidiv, Oren Tsur and Ari Rappoport. 2010. Enhanced Sentiment Learning Using Twitter Hashtags and Smileys. Coling 2010. Xiaowen Ding and Bing Liu. 2007. The Utility of Linguistic Rules in Opinion Mining. SIGIR-2007 (poster paper), 23-27 July 2007, Amsterdam. Alec Go, Richa Bhayani, Lei Huang. 2009. Twitter Sentiment Classification using Distant Supervision. Vasileios Hatzivassiloglou and Kathleen.R. McKeown. 2002. Predicting the semantic orientation of adjectives. In Proceedings of the 35th ACL and the 8th Conference of the European Chapter of the ACL. Minqing Hu and Bing Liu. 2004. Mining and summarizing customer reviews. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD-2004, full paper), Seattle, Washington, USA, Aug 22-25, 2004. Thorsten Joachims. Making Large-scale Support Vector Machine Learning Practical. In B. SchÄolkopf, C. J. C. Burges, and A. J. Smola, editors, Advances in kernel methods: support vector learning, pages 169184. MIT Press, Cambridge, MA, USA, 1999. Kim and Eduard Hovy 2006. Extracting opinions, opinion holders, and topics expressed in online news media text, In Proc. of ACL Workshop on Sentiment and Subjectivity in Text, pp. 1-8, Sydney, Aus- Soo-Min tralia. McDonald, F. Pereira, K. Ribarov, and J. Hajiˇc. 2005. Non-projective dependency parsing using spanning tree algorithms. In Proc. HLT/EMNLP. Tetsuya Nasukawa, Jeonghee Yi. 2003. Sentiment analysis: capturing favorability using natural language processing. In Proceedings of K-CAP. Bo Pang, Lillian Lee. 2004. A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts. In Proceedings of ACL 2004. Ryan Bo Pang, Lillian Lee, Shivakumar Vaithyanathan. 2002. Thumbs up? Sentiment Classification using Machine Learning Techniques. Ravi Parikh and Matin Movassate. 2009. Sentiment Analysis of User-Generated Twitter Updates using Various Classification Techniques. Wee. M. Soon, Hwee. T. Ng, and Danial. C. Y. Lim. 2001. A Machine Learning Approach to Coreference Resolution of Noun Phrases. Computational Linguistics, 27(4):521–544. Peter D. Turney. 2002. Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews. In proceedings of ACL 2002. Janyce Wiebe. 2000. Learning subjective adjectives from corpora. In Proceedings of AAAI-2000. Theresa Wilson, Janyce Wiebe, Paul Hoffmann. 2005. Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis. In Proceedings of NAACL 2005. 160