Analyzing Sentiment in Classical Chinese Poetry

Abstract

Although sentiment analysis in Chinese social media has attracted a lot of interest in recent years, it has been less explored in traditional Chinese literature (e.g., classical Chinese poetry) due to the lack of sentiment lexicon resources. In this paper, we propose a weakly supervised approach based onWeighted Personalized PageRank (WPPR) to create a sentiment lexicon for classical Chinese poetry. We evaluate our lexicon intrinsically and extrinsically. We show that our graphbased approach outperforms a previous well-known PMI-based approach (Turney and Littman, 2003) on both evaluation settings. On the basis of our sentiment lexicon, we analyze sentiment in the Complete Anthology of Tang Poetry. We extract topics associated with positive (negative) sentiment using a position-aware sentimenttopic model. We further compare sentiment among different poets in Tang Dynasty (AD 618 - 907). c 2015 Association for Computational Linguistics and The Asian Federation of Natural Language Processing.