Determining Fuzzy Membership for Sentiment Classification: A Three-Layer Sentiment Propagation Model.
Determining Fuzzy Membership for Sentiment Classification: A Three-Layer Sentiment Propagation Model.
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确定情感分类的模糊隶属度：三层情感传播模型
DOI：
10.1371/journal.pone.0165560
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发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
Li D
中科院分区：
文献类型：
--
作者：
Zhao C;Wang S;Li D
在论坛、博客、twitter账户和购物网站中存在着大量的评论文档。分析这些评论文档中隐藏的情感信息对消费者和制造商都非常有用。与使用双极情感极性相比，使用情感评分可以更详细地描述评论的情感取向和情感强度。用于计算评论情感分数的现有方法经常使用情感词典或句子、段落和文档中的特征的位置。为了获得更准确的评论文档的情感得分，提出了一种三层情感传播模型（TLSPM），该模型使用三种相互关系，即文档，主题和单词之间的相互关系。首先，我们在文档、主题和单词之间使用九个关系成对矩阵。在TLSPM中，我们假设情感邻居在情感传播网络中倾向于具有相同的情感极性和相似的情感强度。然后，我们依次实现文档、主题和单词之间的情感传播过程。最后，我们可以通过一个连续的迭代过程中获得稳定的情感评分的文件。直觉可能表明，具有较强情感强度的文档比具有较弱情感强度的文档对分类的贡献更大。因此，我们使用TLSPM获得的文档的模糊隶属度作为文本的权重来训练模糊支持向量机模型（FSVM）。通过与支持向量机（SVM）和其他四种模糊隶属度确定方法的比较，结果表明，用TLSPM训练的FSVM能够提高情感分类的有效性。此外，使用TLSPM训练的FSVM可以降低七个情感评级预测数据集的均方误差（MSE）。
Enormous quantities of review documents exist in forums, blogs, twitter accounts, and shopping web sites. Analysis of the sentiment information hidden in these review documents is very useful for consumers and manufacturers. The sentiment orientation and sentiment intensity of a review can be described in more detail by using a sentiment score than by using bipolar sentiment polarity. Existing methods for calculating review sentiment scores frequently use a sentiment lexicon or the locations of features in a sentence, a paragraph, and a document. In order to achieve more accurate sentiment scores of review documents, a three-layer sentiment propagation model (TLSPM) is proposed that uses three kinds of interrelations, those among documents, topics, and words. First, we use nine relationship pairwise matrices between documents, topics, and words. In TLSPM, we suppose that sentiment neighbors tend to have the same sentiment polarity and similar sentiment intensity in the sentiment propagation network. Then, we implement the sentiment propagation processes among the documents, topics, and words in turn. Finally, we can obtain the steady sentiment scores of documents by a continuous iteration process. Intuition might suggest that documents with strong sentiment intensity make larger contributions to classification than those with weak sentiment intensity. Therefore, we use the fuzzy membership of documents obtained by TLSPM as the weight of the text to train a fuzzy support vector machine model (FSVM). As compared with a support vector machine (SVM) and four other fuzzy membership determination methods, the results show that FSVM trained with TLSPM can enhance the effectiveness of sentiment classification. In addition, FSVM trained with TLSPM can reduce the mean square error (MSE) on seven sentiment rating prediction data sets.
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