Abstract
Traditional sentiment analysis has been focused on predicting the polarity of texts as positive or negative at different granularity. This broad categorization does not account for informativeness of the underlying text. For many real-world applications such as social listening, brand monitoring and e-commerce platforms, the opinions that really matter are the informative opinions describing why something is good or bad. In this paper, we try to understand the properties of complaints and praises which is an informative subset of the negative and positive categories. Our analysis in the context of user reviews shows that complaints and praises have distinct properties that differentiate it from positive only or negative only sentences.
Cite
CITATION STYLE
Ganesan, K., & Zhou, G. (2016). Linguistic understanding of complaints and praises in user reviews. In Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, WASSA 2016 at the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2016 (pp. 109–114). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w16-0418
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