Chinese Sentence-Level Sentiment Classification Based on Fuzzy Sets
Chinese Sentence-Level Sentiment Classification Based on Fuzzy Sets
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发表时间：
2010-08
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通讯作者：
G. Fu;Xin Wang
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作者：
G. Fu;Xin Wang
提出了一种基于模糊集理论的汉语句子级情感分类方法。与传统的基于主题的文本分类技术相比，模糊集理论提供了一种直观的方法来建模情感极性类之间的内在模糊性。为了实现模糊情感分类，我们首先提出了一种从细到粗的策略来估计句子的情感强度。然后，我们定义了三个模糊集来表示各自的情绪极性类别，即积极、消极和中性情绪。基于句子的情感强度，我们进一步构建隶属函数来表示一个固执己见的句子在不同模糊集中的程度。最后，根据最大隶属度原则确定句子级极性。我们表明，我们的方法可以在ntir -6的中文意见分析试点任务的测试集上取得令人满意的性能。
This paper presents a fuzzy set theory based approach to Chinese sentence-level sentiment classification. Compared with traditional topic-based text classification techniques, the fuzzy set theory provides a straightforward way to model the intrinsic fuzziness between sentiment polarity classes. To approach fuzzy sentiment classification, we first propose a fine-to-coarse strategy to estimate sentence sentiment intensity. Then, we define three fuzzy sets to represent the respective sentiment polarity classes, namely positive, negative and neutral sentiments. Based on sentence sentiment intensities, we further build membership functions to indicate the degrees of an opinionated sentence in different fuzzy sets. Finally, we determine sentence-level polarity under maximum membership principle. We show that our approach can achieve promising performance on the test set for Chinese opinion analysis pilot task at NTCIR-6.