Sentiment Classification Using Word Sub-sequences and Dependency Sub-trees
Sentiment Classification Using Word Sub-sequences and Dependency Sub-trees
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DOI：
10.1007/11430919_37
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发表时间：
2005-05
期刊：
影响因子：
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通讯作者：
Shotaro Matsumoto;Hiroya Takamura;M. Okumura
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文献类型：
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作者：
Shotaro Matsumoto;Hiroya Takamura;M. Okumura
文档情感分类是根据文档观点的正面或负面极性（有利或不利）对文档进行分类的任务。我们建议使用句子中单词之间的句法关系进行文档情感分类。具体来说，我们使用文本挖掘技术从文档数据集中的句子中提取频繁的单词子序列和依存子树，并将它们用作支持向量机的特征。在电影评论数据集的实验中，我们的分类器获得了使用这些数据发布的最佳结果。
Document sentiment classification is a task to classify a document according to the positive or negative polarity of its opinion (favorable or unfavorable). We propose using syntactic relations between words in sentences for document sentiment classification. Specifically, we use text mining techniques to extract frequent word sub-sequences and dependency sub-trees from sentences in a document dataset and use them as features of support vector machines. In experiments on movie review datasets, our classifiers obtained the best results yet published using these data.