Sentiment Analysis and Sentiment Change Analysis
Sentiment Analysis and Topic Sentiment Change Analysis
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:
- Clement Yu (UIC, Faculty)
- Wei Zhang (UIC, PhD student, Graduated)
- Eduard C. Dragut (UIC, PhD student, Graduated)
- Lifeng Jia (UIC, PhD student, graduated)
- Weiyi Meng (BU, Faculty)
- Yu Jiang (BU, PhD student)
- 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.
- 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.
- 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.
- 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,
- 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.
- 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.
- 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.
- 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.
- 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,
- 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.
- 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,
Last change: September 3, 2015 / email@example.com