TY - GEN
T1 - Topic sentiment mixture
T2 - 16th International World Wide Web Conference, WWW2007
AU - Mei, Qiaozhu
AU - Ling, Xu
AU - Wondra, Matthew
AU - Su, Hang
AU - Zhai, Chengxiang
PY - 2007
Y1 - 2007
N2 - In this paper, we define the problem of topic-sentiment analysis on Weblogs and propose a novel probabilistic model to capture the mixture of topics and sentiments simultaneously. The proposed Topic-Sentiment Mixture (TSM) model can reveal the latent topical facets in a Weblog collection, the subtopics in the results of an ad hoc query, and their associated sentiments. It could also provide general sentiment models that are applicable to any ad hoc topics. With a specifically designed HMM structure, the sentiment models and topic models estimated with TSM can be utilized to extract topic life cycles and sentiment dynamics. Empirical experiments on different Weblog datasets show that this approach is effective for modeling the topic facets and sentiments and extracting their dynamics from Weblog collections. The TSM model is quite general; it can be applied to any text collections with a mixture of topics and sentiments, thus has many potential applications, such as search result summarization, opinion tracking, and user behavior prediction.
AB - In this paper, we define the problem of topic-sentiment analysis on Weblogs and propose a novel probabilistic model to capture the mixture of topics and sentiments simultaneously. The proposed Topic-Sentiment Mixture (TSM) model can reveal the latent topical facets in a Weblog collection, the subtopics in the results of an ad hoc query, and their associated sentiments. It could also provide general sentiment models that are applicable to any ad hoc topics. With a specifically designed HMM structure, the sentiment models and topic models estimated with TSM can be utilized to extract topic life cycles and sentiment dynamics. Empirical experiments on different Weblog datasets show that this approach is effective for modeling the topic facets and sentiments and extracting their dynamics from Weblog collections. The TSM model is quite general; it can be applied to any text collections with a mixture of topics and sentiments, thus has many potential applications, such as search result summarization, opinion tracking, and user behavior prediction.
KW - Mixture model
KW - Sentiment analysis
KW - Topic models
KW - Topic-sentiment mixture
KW - Weblogs
UR - http://www.scopus.com/inward/record.url?scp=35348882767&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=35348882767&partnerID=8YFLogxK
U2 - 10.1145/1242572.1242596
DO - 10.1145/1242572.1242596
M3 - Conference contribution
AN - SCOPUS:35348882767
SN - 1595936548
SN - 9781595936547
T3 - 16th International World Wide Web Conference, WWW2007
SP - 171
EP - 180
BT - 16th International World Wide Web Conference, WWW2007
Y2 - 8 May 2007 through 12 May 2007
ER -