Sentiment Lexicon Expansion Based on Neural PU Learning, Double Dictionary Lookup, and Polarity Association
Sentiment Lexicon Expansion Based on Neural PU Learning, Double Dictionary Lookup, and Polarity Association
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DOI：
10.18653/v1/d17-1059
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发表时间：
2017-09
期刊：
影响因子：
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通讯作者：
Yasheng Wang;Yang Zhang;Bing Liu
中科院分区：
文献类型：
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作者：
Yasheng Wang;Yang Zhang;Bing Liu
虽然存在许多不同语言的情感词汇，但大多数都不全面。在最近的一个情感分析应用程序中，我们使用了一个大型的中文情感词典，发现它错过了社交媒体中的大量情感词。这促使我们对情感词汇扩展进行新的尝试。本文首先提出了一个PU学习问题，这是一个新的提法。然后，它提出了一个新的PU学习方法适合我们的问题，使用神经网络。结果进一步增强了一个新的字典为基础的技术和一种新的极性分类技术。实验结果表明，该方法的性能大大优于基线方法。
Although many sentiment lexicons in different languages exist, most are not comprehensive. In a recent sentiment analysis application, we used a large Chinese sentiment lexicon and found that it missed a large number of sentiment words in social media. This prompted us to make a new attempt to study sentiment lexicon expansion. This paper first poses the problem as a PU learning problem, which is a new formulation. It then proposes a new PU learning method suitable for our problem using a neural network. The results are enhanced further with a new dictionary-based technique and a novel polarity classification technique. Experimental results show that the proposed approach outperforms baseline methods greatly.