一种基于混合特征的中文情感词典扩展方法

覆盖面广且领域适应性好的情感词典可以有效提高文本情感分析效能。设计了基于连词语言特征和词性特征向量统计特征的中文情感词典扩展算法,提出了综合两种方法的混合特征算法。算法计算得到词语的细粒度的积极和消极情感极性值,并对通用情感词典在领域内进行扩展以提高覆盖度,对词典进行领域内调整以提高适应性。实验结果表明,算法在领域内扩展获得的词典比通用情感词典覆盖度和适应性更好,在情感分类任务中性能接近有监督方法。

A Chinese sentiment lexicon extension method based on mixing features

The performance of sentiment analysis can be improved effectively with the help of a wide-coverage and good domain-adapting sentiment lexicon. We firstly design two Chinese sentiment lexicon extension algorithms, which base on conjunctions feature and POS-vector statistical feature respectively. We then propose an integrated mixing feature method that combines the two algorithms. Fine-grained positive and negative values can be calculated for opinion words, the coverage of the lexicon can be improved within a domain, and the adaption of the lexicon can be improved with adjustment in the domain. Experimental results show that the extension lexicon has wider coverage and better adaption than a general lexicon in a domain, and the proposal's performance of sentiment classification can approximate that of a supervised method.