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Fine-Grained Sentiment Analysis Based on Sentiment Disambiguation

机译:基于情感歧义的细粒度情感分析

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In this paper research on the problem of dynamic polarity change in review analysis. Firstly, Apriori algorithm is used to expand the sentiment ambiguous words based on context, and construct the sentiment ambiguous lexicon, namely triples of (sentiment object, sentiment word, sentiment polarity). Then make use of the condition random field model (CRFs) extracted emotional elements from comments, to fine-grained sentiment orientation analysis based on the sentiment ambiguous lexicon. Experimental results over product corpus in mobile-phone and computer domains show that the feasibility of the proposed method, and helps improve the accuracy of sentiment analysis.
机译:本文对动态极性变化问题进行综述分析研究。首先,使用Apriori算法基于上下文扩展情感歧义词,构造情感歧义词表,即(情感对象,情感词,情感极性)的三元组。然后利用条件随机场模型(CRF)从评论中提取情感元素,基于情感歧义词典对情感取向进行细化分析。在手机和计算机领域的产品语料库上的实验结果表明,该方法是可行的,有助于提高情感分析的准确性。

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