Fuzzy Sentiment Membership Determining for Sentiment Classification
Fuzzy Sentiment Membership Determining for Sentiment Classification
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DOI：
10.1109/icdmw.2014.137
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发表时间：
2014-12
期刊：
影响因子：
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通讯作者：
Chuanjun Zhao;Suge Wang;Deyu Li
中科院分区：
文献类型：
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作者：
Chuanjun Zhao;Suge Wang;Deyu Li
传统的支持向量机使用相同的权重来处理所有样本。因此，它对噪声数据非常敏感。而模糊支持向量机对分类贡献较小的样本赋予较低的权重，从而有利于减少有噪和不重要的数据对分类准确率的影响。在本文中，我们提出了一种新的模糊情感隶属度确定方法来解决情感分类任务。我们假设强强度文本对情感分类的贡献更大，而弱强度文本对分类不重要。为了获得评论文本的模糊情感隶属度，提出了一种三层情感传播模型。首先，根据文本、主题和词语之间的相互关系计算文本的情感得分，并确定情感得分的绝对值作为文本的模糊情感隶属度。然后，我们训练一个模糊支持向量机来分类测试数据集的样本。最后，我们对来自亚马逊购物网站的四个英文评论数据集进行了实验。实验结果表明，该方法能有效提高情感分类的准确率。
Traditional support vector machine treats all samples using the same weight. Therefore it is very sensitive to noisy data. While the fuzzy support vector machine assigns lower weights to the samples which make small contributions to classification, thus it is beneficial to reduce the effects of noisy and unimportant data on the classification accuracy rate. In this paper, we propose a novel fuzzy sentiment membership determining method for solving sentiment classification task. We assume that strong intensity texts make more contributions to sentiment classification, while weak intensity texts are unimportant for the classification. In order to get the fuzzy sentiment membership of review texts, this paper proposes a three-layer sentiment propagation model. Firstly, we calculate the sentiment score of texts by the interrelations of the texts, topics and words, and ensure that the absolute value of sentiment score as the fuzzy sentiment membership degree of texts. Then, we train a fuzzy support vector machine to classify the samples from the test data sets. Finally, we conduct some experiments on four English reviews data sets from Amazon shopping websites. The experimental results show that the proposed method can improve the accuracy of sentiment classification effectively.