Short Text Sentiment Classification Based on Context Reconstruction
基于上下文重构的短文本情感极性判别研究

Synonymy and polysemy present a challenge to effective natural language processing, especially in the situations of context absence and sparse feature in short texts, widened semantic gap between low-level text features representation and high-level interpretation. In this work, short texts were reorganized into special context, i.e., the implied internal relationship such as time and space, and a novel two-step scheme for semantic orientation detection based on the special context was proposed. In the first step, the short texts were reorganized into special contexts by the implied internal relationship. In the second step, the unknown short text was categorized into a special context and labeled a polarity tag using the inner semantic orientation classifier. We firstly discussed the effect of special context after a sentiment classification framework based on naive Bayes classifier was presented. Then an enhancement classification method was given using field concept, which was expanded to special context. Finally, a special context reorganizing method was proposed based on genetic algorithm. Theoretical analysis shows the proposed methods can reduce the sample error and approximation error under some constraints. The experimental results in real corpora show the effectiveness of the proposed method.