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OPEN DOMAIN TARGETED SENTIMENT CLASSIFICATION USING SEMISUPERVISED DYNAMIC GENERATION OF FEATURE ATTRIBUTES
OPEN DOMAIN TARGETED SENTIMENT CLASSIFICATION USING SEMISUPERVISED DYNAMIC GENERATION OF FEATURE ATTRIBUTES
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机译：使用特征属性的半监督动态生成的开放域目标情感分类
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摘要
Methods for classification of microblogs using semi-supervised open domain targeted sentiment classification. A hidden Markov model support vector machine (SVM HMM) is trained with a training dataset combined with discrete features. A portion of the training dataset is clustered by k-means clustering to generate cluster IDs which are normalized and combined with the discrete features. After formatting, the combined dataset is applied to the SVM HMM and the C parameter, which is optimized by calculating a zero-one error at each iteration. The open domain targeted sentiment classification methods uses less labelled data than previous sentiment analysis techniques, thus decreasing processing costs. Additionally, a supervised learning model for improving the accuracy of open domain targeted sentiment classification is presented using an SVM HMM.
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