廣義知網詞彙意見極性的預測

Predicting the Semantic Orientation of Terms in E-HowNet

詞彙的意見極性是句子及文件層次意見分析的重要基礎，雖然目前已經存在一些人工標記的中文情緒字典，但如何自動標記詞彙的意見極性，仍是一個重要的工作。這篇論文的目的是為廣義知網的詞彙自動標記意見極性。我們運用監督式機器學習的方法，抽取不同來源的各種有用特徵並加以整合，來預測詞彙的意見極性。實驗結果顯示，廣義知網詞彙意見極性預測的準確率可到達92.33%，這個結果跟人的標記準確率不相上下。

The semantic orientation of terms is fundamental for sentiment analysis in sentence and document levels. Although some Chinese sentiment dictionaries are available, how to predict the orientation of terms automatically is still important. In this paper, we predict the semantic orientation of terms of E-HowNet. We extract many useful features from different sources to represent a Chinese term in E-HowNet, and use a supervised machine learning algorithm to predict its orientation. Our experimental result showed that the proposed approach can achieve 92.33% accuracy, which is comparable to the accuracy of human taggers.