Phonetic-enriched Text Representation for Chinese Sentiment Analysis with Reinforcement Learning
This addresses sentiment analysis for Chinese language users by leveraging unique phonetic properties, though it is incremental as it builds on existing text and visual feature methods.
The paper tackled Chinese sentiment analysis by incorporating phonetic features, such as intonation and pronunciation, into text representations, achieving state-of-the-art performance on five datasets with significant improvements over existing methods.
The Chinese pronunciation system offers two characteristics that distinguish it from other languages: deep phonemic orthography and intonation variations. We are the first to argue that these two important properties can play a major role in Chinese sentiment analysis. Particularly, we propose two effective features to encode phonetic information. Next, we develop a Disambiguate Intonation for Sentiment Analysis (DISA) network using a reinforcement network. It functions as disambiguating intonations for each Chinese character (pinyin). Thus, a precise phonetic representation of Chinese is learned. Furthermore, we also fuse phonetic features with textual and visual features in order to mimic the way humans read and understand Chinese text. Experimental results on five different Chinese sentiment analysis datasets show that the inclusion of phonetic features significantly and consistently improves the performance of textual and visual representations and outshines the state-of-the-art Chinese character level representations.