Titlebook: Database Systems for Advanced Applications; DASFAA 2015 Internat An Liu,Yoshiharu Ishikawa,Muhammad Aamir Cheema Conference proceedings 201

凝结剂

Sentiment Detection of Short Text via Probabilistic Topic Modeling text. To solve the problems of sparsity and context-dependency, we extract hidden topics behind the text and associate different words by the same topic. Evaluation on sentiment detection of short text verified the effectiveness of the proposed method.

JAUNT

不能根除

Intensive Maximum Entropy Model for Sentiment Classification of Short Textlassification, which generates the probability of sentiments conditioned to short text by employing intensive feature functions. Experimental evaluations using real-world data validate the effectiveness of the proposed model on sentiment classification of short text.

揉杂

压舱物

CHYME

狗窝

Dialogues, Reasons and Endorsementn item is generated by ratings of these opinion leaders and the active user. Experimental results based on Epinions data set demonstrated that the prediction accuracy of our method outperforms other approach.

溺爱

A Second Look at the ASCAD Databases on five distinct semantic relations. Experiments show our average precision is ., compared to TE/ASE method with average precision of .. Besides, we can acquire 3 paraphrases more than TE/ASE method per input.