Title :
Feature selection for Chinese online reviews sentiment classification
Author :
Xian Chen ; Jing Ma ; Yueming Lu
Author_Institution :
Key Lab. of Trustworthy Distrib. Comput. & Service (BUPT), BUPT, Beijing, China
Abstract :
Considering that traditional feature selection methods (DF, MI and IG) usually lost useful information, we propose the Feature Selection for Chinese Online Reviews Sentiment Classification (FSCSC), FSCSC takes empirical analysis into account and focus on how to effectively select different types of features based on statistical approaches to improve sentiment classification performance. FSCSC was tested on a Chinese online reviews corpus with a size of 4000 documents. The experiment indicates that FSCSC can improve the classification effectiveness.
Keywords :
behavioural sciences computing; document handling; feature selection; pattern classification; statistical analysis; Chinese online reviews corpus; Chinese online reviews sentiment classification; FSCSC; classification effectiveness; documents; empirical analysis; sentiment classification performance; statistical approaches; traditional feature selection methods; Accuracy; Learning systems; Niobium; Power capacitors; Sentiment analysis; Support vector machines; Text categorization; empirical analysis; feature selection; sentiment classification;
Conference_Titel :
Computational Problem-solving (ICCP), 2013 International Conference on
Conference_Location :
Jiuzhai
DOI :
10.1109/ICCPS.2013.6893490
