NEUOM: Identifying Opinionated Sentences in Chinese and English Text

NEUOM: Identifying Opinionated Sentences in Chinese and English Text Zhang, Ke Wang, Muhua Zhu, Tong Xiao, Jingbo Zhu Natural Language Processing Lab, Northeastern University {zhangcl, xiaotong, zhujingbo}@mail.neu.edu.cn [email protected], [email protected] Abstract This paper introduces our NEUOM system which participates in the opinionated sentence detection task, one of evaluation tasks in Multilingual Opinion Analysis Task (MOAT) of NTCIR-7. NEUOM system adopts a sentiment lexicon-based(SLB) approach to identifying opinionated sentences in a Chinese text and English text. For English task, a machine learning algorithm, naïve Bayesian classification model, is also tried with the use of the English training corpora, such as MPQA and NTCIR-6 data set. Experimental results show that in the English task SLB method achieved better F1 performance than Naïve Bayesian model.