摘要： 在研究区分性关键词提取方法的基础上,对维吾尔语中的生气和高兴等常见情感类型进行基于文本句子的情感分类研究。结合维吾尔文本句子中的情感表达特点,以词频和文档频率作为基本统计量,通过计算同一词语在不同组合统计量下的类间差异得到区分性关键词,并基于这些关键词进行特征提取和区分性情感模型构建。从维吾尔语电影字幕、小说等文本库中提取生气和高兴2 种情感构造实验数据集,并验证所提出的情感分类方法。实验结果表明,基于区分性关键词的建模方法能有效地对维吾尔文本句子进行情感分类。
关键词:
维吾尔语,
区分性关键词,
文本句子,
情感分类,
差异性统计量
Abstract: This paper presents a classification approach for Uyghur text sentiment,such as angry and happy,based on
discriminative key word extraction. Combined with the characteristics of sentiment expression in Uyghur text,the term frequency and document frequency are derived as primary statistics. Various discriminative statistics which reflect the discrepancy of the positive and negative sentiment datasets are derived from the primary statistics for each vocabulary word, and are used to extract discriminative key words. Features are extracted based on these keywords and are used to train discriminative sentiment models. This paper builds a sentiment text database by excerpting two sentiments:angriness and happiness from Uyghur movie transcriptions and novels,and verifies the proposed approach. Experimental results show that the method based on discriminative keyword extraction is effective in Uyghur text sentence sentiment classification.
Key words:
Uyghur language,
discriminative keyword;text sentence;sentiment classification;difference statistics
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