Building Chinese Affective Resources in Valence-Arousal Dimensions

Proceedings of NAACL-HLT 2016, pages 540–545, San Diego, California, June 12-17, 2016. c⃝2016 Association for Computational Linguistics Building Chinese Affective Resources in Valence-Arousal Dimensions Liang-Chih Yu1,3, Lung-Hao Lee4, Shuai Hao5, Jin Wang2,3,6, Yunchao He2,3,6, Jun Hu5, K. Robert Lai2,3 and Xuejie Zhang6 1Department of Information Management, Yuan Ze University, Taiwan 2Department of Computer Science & Engineering, Yuan Ze University, Taiwan 3Innovation Center for Big Data and Digital Convergence Yuan Ze University, Taiwan 4Information Technology Center, National Taiwan Normal University, Taiwan 5School of Software, Nanchang University, Jiangxi, P.R. China 6School of Information Science and Engineering, Yunnan University, Yunnan, P.R. China Contact: lcyu@saturn.yzu.edu.tw Abstract An increasing amount of research has recently focused on representing affective states as continuous numerical values on multiple di- mensions, such as the valence-arousal (VA) space. Compared to the categorical approach that represents affective states as several clas- ses (e.g., positive and negative), the dimen- sional approach can provide more fine- grained sentiment analysis. However, affec- tive resources with valence-arousal ratings are still very rare, especially for the Chinese lan- guage. Therefore, this study builds 1) an af- fective lexicon called Chinese valence-arousal words (CVAW) containing 1,653 words, and 2) an affective corpus called Chinese valence- arousal text (CVAT) containing 2,009 sen- tences extracted from web texts. To improve the annotation quality, a corpus cleanup pro- cedure is used to remove outlier ratings and improper texts. Experiments using CVAW words to predict the VA ratings of the CVAT corpus show results comparable to those ob- tained using English affective resources. 1 Introduction Sentiment analysis has emerged as a leading tech- nique to automatically identify affective infor- mation from texts (Pang and Lee, 2008; Calvo and D'Mello, 2010; Liu, 2012; Feldman, 2013). In sen- timent analysis, affective states are generally rep- resented using either categorical or dimensional approaches. The categorical approach represents affective states as several discrete classes such as positive, neutral, negative, and Ekman’s six basic emotions (e.g., anger, happiness, fear, sadness, disgust and surprise) (Ekman, 1992). Based on this representa- tion, various practical applications have been de- veloped such as aspect-based sentiment analysis (Schouten and Frasincar, 2016; Pontiki et al., 2015), Twitter sentiment analysis (Saif et al., 2013; Rosenthal et al., 2015), deceptive opinion spam de- tection (Li et al., 2014), and cross-lingual portabil- ity (Banea et al., 2013; Xu et al., 2015). The dimensional approach represents affective states as continuous numerical values in multiple dimensions, such as valence-arousal (VA) space (Russell, 1980), as shown in Fig. 1. The valence represents the degree of pleasant and unpleasant (i.e., positive and negative) feelings, while the arousal represents the degree of excitement and calm. Based on this representation, any affective state can be represented as a point in the VA coor- dinate plane. For many application domains (e.g., product reviews, political stance detection, etc.), it can be useful to identify highly negative-arousing and highly positive-arousing texts because they are usually of interest to many users and should be given a higher priority. Dimensional sentiment 540 analysis can accomplish this …

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