基于领域情感词典特征表示的细粒度意见挖掘

细粒度意见挖掘的主要目标是从观点文本中获取情感要素并判断情感倾向。现有方法大多基于序列标注模型,但很少利用情感词典资源。该文提出一种基于领域情感词典特征表示的细粒度意见挖掘方法,使用领域情感词典在观点文本上构建特征表示并将其加入序列标注模型的输入部分。首先构建一份新的电商领域情感词典,然后在电商评论文本真实数据上,分别为条件随机场(CRF)和双向长短期记忆-条件随机场(BiLSTM-CRF)这两种常用序列标注模型设计基于领域情感词典的特征表示。实验结果表明,基于电商领域情感词典的特征表示方法在两种模型上都取得了良好的效果,并且超过其他情感词典。

Fine-grained Opinion Mining Based on Feature Representation of Domain Sentiment Lexicon

Fine-grained opinion mining aims at detecting sentiment units and determining sentiment polarity from opinion text. Recent methods are mostly based on sequence labeling models, rarely using the information of sentiment lexicon resources. This paper proposes a fine-grained opinion mining method based on feature representation of domain sentiment lexicon. It generates feature representation by using domain sentiment lexicon, applying it as the input of sequence labeling model. We build a new sentiment lexicon in E-commerce domain, and then we design feature representation of domain sentiment lexicon for CRF and BiLSTM-CRF. Experiments on E-commerce reviews show that our proposed method performs well on both models and outperforms the method based on other lexica.