Sentiment Analysis and Sentiment Change Analysis

Sentiment Analysis and Topic Sentiment Change Analysis

Project Overview

Public opinions and sentiments can have major impact on our society. They can affect the sales of products, the change of government policy, and even people's vote in elections. Thus, it is of high significance to study sentiment analysis (also known as opinion retrieval). In the age of the Web, more and more people choose to express their opinions on a wide range of topics on the Web in the forms of blogs, product/service reviews, and comments. Sentiment analysis aims to retrieve or mine opinions about different topics from text documents.

A new emphasis of this project is on Topic Sentiment Change Analysis (TSCA). TSCA consists of several significant sub-problems. The first is to determine the sentiment of a topic from a corpus of relevant opinionated documents. The second is to identify significant changes of the sentiment about a topic over time. The third is to identify the causes behind each of the significant sentiment changes.

We believe that the ability to precisely identify the reasons behind significant sentiment changes in a timely manner will make sentiment analysis much more practically useful, which in turn will make sentiment analysis a much more attractive research area not only for computer scientists but also for political scientists, social scientists and linguists. Thus, this project has the potential to lead to transformative changes in sentiment analysis research.


The following people have participated/are participating in this project:

Related Publications

  1. Wei Zhou, Clement Yu, Neil Smalheiser, Vetle Torvik and Jie Hong. Knowledge Intensive Conceptual Retrieval and Passage Extraction of Biomedical Literature. ACM SIGIR Conference, pp.655-662, July 2007.
  2. Wei Zhang, Clement Yu, Weiyi Meng. Opinion Retrieval from Blogs. ACM Sixteenth Conference on Information and Knowledge Management (CIKM), pp.831-840, Lisboa, Portugal, November 2007.
  3. Wei Zhang, Lifeng Jia, Clement Yu, Weiyi Meng. Improve the Effectiveness of the Opinion Retrieval and Opinion Polarity Classification. ACM 17th Conference on Information and Knowledge Management (CIKM 2008), poster paper, pp.1415-1416, Napa Valley, California, October 2008.
  4. Lifeng Jia, Clement Yu, Weiyi Meng. The Effect of Negation on Sentiment Analysis and Retrieval Effectiveness. 18th ACM Conference on Informationand Knowledge Management (CIKM 2009), Hong Kong, China, pp.1827-1830, November 2009.
  5. Eduard Dragut, Clement Yu, Prasad Sistla, Weiyi Meng. Construction of a Sentimental Word Dictionary. 19th ACM Conference on Informationand Knowledge Management (CIKM 2010), pp.1761-1764, Toronto, Canada, October 2010.
  6. Yu Jiang, Weiyi Meng, and Clement Yu. Topic Sentiment Change Analysis. 7th International Conference on Machine Learning and Data Mining (MLDM), pp.443-457, New York City, August 2011.
  7. Eduard C. Dragut, Hong Wang, Clement Yu, A. Prasad Sistla and Weiyi Meng. Polarity Consistency Checking for Sentiment Dictionaries. Annual Meeting of the Association for Computational Linguistics (ACL), pp.997-1005, Jeju Island, Korea, July 2012.
  8. Lifeng Jia, Clement Yu, Weiyi Meng. Faceted Models of Blog Feeds . Proceedings of the ACM Conference on Information and Knowledge Management (CIKM), pp.2279-2284, San Francisco, CA, October 2013.
  9. Lifeng Jia, Clement Yu, Weiyi Meng. The Impacts of Structural Difference and Temporality of Tweets on Retrieval Effectiveness . ACM Transactions on Information Systems (TOIS), 31(4), 21, December 2013.
  10. Yu Jiang, Xian Li, and Weiyi Meng. DiscWord: Learning Discriminative Topics. 2014 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2014), Warsaw, Poland, August 2014.
  11. Eduard Dragut, Hong Wang, Clement Yu, Prasad Sistla, and Weiyi Meng. Polarity consistency checking for domain independent sentiment dictionaries. IEEE Transactions on Knowledge and Data Engineering (TKDE), 27(3):838-851, March 2015.

Last change: September 3, 2015 /