Sentiment Analysis for Tang Poetry Based on Imagery Aided and Classifier Fusion

This paper aims to do sentiment analysis for Tang poetry from the perspective of text mining. Most previous works just focus on the literariness of Chinese poetry or establish language models statistically, which ignore the features of sentiment and specific applications. We propose a sentiment analysis system for Tang poetry based on imagery aided and classifier fusion. Especially, we extract sentimental imageries at two levels: character and word, and bring them into sentiment analysis. In addition, classifier fusion is adopted in this paper to improve classification performance. Experiments show the effectiveness of our model and our method is superior to the traditional method.