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引用次数: 1
摘要
随着社交媒体服务的兴起,情感分析成为近年来一个重要的研究课题。利用对未来舆论趋势的预测来调整当前的商业策略是情感发现最有价值的应用。先前提出的语言模型CCLM用于在语料库中产生大量术语,其主要缺点是情感分类器花费太多时间进行搜索,从而减缓了情感判断。本研究试图提高情感分类器的精度和性能,提出了一种基于人类习惯的习语表达方法,并利用结合Jieba习惯语言模型(Combined Jieba Customary Language Model, CJCLM)构建一种新的句子级情感分类器。在实验中,所有的中文句子都是从Plurk平台上检索的,以展示CJCLM对执行时间的改善。
Using idiomatic expression for Chinese sentiment analysis
The rising of social media services causes Sentiment Analysis is becoming a critical research issue in recent years. Using the prediction of future opinion trends to adjust current business strategies is the most valuable application of emotion discovery. The major drawback of the previously proposed language model, CCLM, used to produce a larger number of terms in corpus causes the emotion classifier slowdown emotion judgement by spending too much time on searching. This research tried to improve the precision and performance of the emotion classifier, the idiomatic expression of human habit has been proposed to promote a novel sentence-level emotion classifier by using the Combined Jieba Customary Language Model (CJCLM). In the experiment, all Chinese sentences are retrieved from Plurk platform to present the improvement of the execution time by CJCLM.