Exploiting effective features for chinese sentiment classification

A survey on opinion mining and sentiment analysis: Tasks, approaches and applications

“…These kinds of partof-speech are known as opinion or sentiment word. Thus, extracting opinion words also follows machine learning based [61,63,92,100], lexicon based [68,132,175] and hybrid approaches [8,102].…”

A rule-based approach to emotion cause detection for Chinese micro-blogs

“…In the process of emotion analysis, identifying the multi-language features in micro-blog posts is necessary to compute the emotion intensity scores. As for the related work, Zhai, Xu, Kang, and Jia (2011) exploited effective features for Chinese sentiment classification, such as sentiment words, substrings,substring-groups, and key-substring-groups features. Li, Pan, Jin, Yang, and Zhu (2012) expanded a few high-confidence sentiment and topic seeds in target domain by the given RAP algorithm.…”

Chinese comments sentiment classification based on word2vec and SVMperf

“…Yessenalina, Yue, and Cardie (2010) proposed a joint two-level approach for document-level sentiment classification that simultaneously extracts subjective sentences and predicts document-level sentiment based on the extracted sentences. Zhai et al (2011b) extracted sentiment-words, substrings, substringgroups and key-substring-groups as features. Wang, Li, Song, Wei, and Li (2011) proposed an effective feature selection method based on fisher's discriminant ratio for subjectivity text sentiment classification.…”

“…In recent years, machine learning-based methods for sentiment classification have been widely adopted due to their excellent performance (Li, Wang, Zhou, & Lee, 2011, 2012Xia, Wang, Hu, Li, & Zong, 2013;Ye, Zhang, & Law, 2009;Yin, Wang, & Zheng, 2012 key issues in machine learning-based methods (Zhai, Xu, Bada, & Peifa, 2011b). Up to this day, a variety of feature extraction methods have been proposed, including single words (Tan & Zhang, 2008), single-character n-grams (Raaijmakers & Kraaij, 2008), multi-word n-grams (Li & Sun, 2007), lexical-syntactic patterns and many other novel models (Xia & Zong, 2010;Zhai, Xu, Jun, & Peifa, 2009;Zhang, Liu, Lim, & O'Brien-Strain, 2010).…”