Interpretable Sentiment Analysis and Text Segmentation for Chinese Language

Abstract

In this paper, we explored the performance of interpretable sentiment analysis models of different combinations for the Chinese text in social media. We made experiment to study how performance varies with the change of combination of different segmentation strategies and dictionary of words or n-grams. We found that with some good combination of segmentation strategies and dictionary of words or n-grams, the result can be improved and overtake the performance of ordinary sentiment analysis model of Chinese language. This way we show the importance of selection of segmentation strategies and dictionary for the sentiment analysis of Chinese text.