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....
Qiaozhu Mei, Xu Ling, Matthew Wondra, Hang Su, Che...
In this paper, we propose a set of novel regression-based approaches to effectively and efficiently summarize frequent itemset patterns. Specifically, we show that the problem of ...
Multinomial distributions over words are frequently used to model topics in text collections. A common, major challenge in applying all such topic models to any text mining proble...
Recent work has shown the feasibility and promise of templateindependent Web data extraction. However, existing approaches use decoupled strategies ? attempting to do data record ...
Jun Zhu, Zaiqing Nie, Ji-Rong Wen, Bo Zhang, Wei-Y...
Temporal Text Mining (TTM) is concerned with discovering temporal patterns in text information collected over time. Since most text information bears some time stamps, TTM has man...