论文标题
通过联合方面主题嵌入基于方面的情绪分析弱监督的情感分析
Weakly-Supervised Aspect-Based Sentiment Analysis via Joint Aspect-Sentiment Topic Embedding
论文作者
论文摘要
基于方面的评论文本情感分析对于以细粒度的方式理解用户反馈具有很大的价值。它通常具有两个子任务：（i）从每次评论中提取各个方面，（ii）通过情感极性对基于方面的评论进行分类。在本文中，我们为基于方面的情感分析提出了一种弱监督的方法，该方法仅使用一些描述每个方面/情感的关键字而无需使用任何标记的示例。现有方法要么仅针对其中一项子任务设计，要么忽略了耦合两者的好处，要么基于可能包含重叠概念的主题模型。我们建议首先学习<情感，方面>联合主题嵌入在嵌入空间中的嵌入，通过强加正规化以鼓励主题独特性，然后使用神经模型来概括单词级别的歧视性信息，通过预先培训基于嵌入的预测和对未修订数据进行自我培训。我们的全面绩效分析表明，我们的方法在基准数据集上生成了质量联合主题，并胜过基线的基线（分别为方面和情感分类的7.4％和5.1％的F1得分增益）。我们的代码和数据可从https://github.com/teapot123/jasen获得。
Aspect-based sentiment analysis of review texts is of great value for understanding user feedback in a fine-grained manner. It has in general two sub-tasks: (i) extracting aspects from each review, and (ii) classifying aspect-based reviews by sentiment polarity. In this paper, we propose a weakly-supervised approach for aspect-based sentiment analysis, which uses only a few keywords describing each aspect/sentiment without using any labeled examples. Existing methods are either designed only for one of the sub-tasks, neglecting the benefit of coupling both, or are based on topic models that may contain overlapping concepts. We propose to first learn <sentiment, aspect> joint topic embeddings in the word embedding space by imposing regularizations to encourage topic distinctiveness, and then use neural models to generalize the word-level discriminative information by pre-training the classifiers with embedding-based predictions and self-training them on unlabeled data. Our comprehensive performance analysis shows that our method generates quality joint topics and outperforms the baselines significantly (7.4% and 5.1% F1-score gain on average for aspect and sentiment classification respectively) on benchmark datasets. Our code and data are available at https://github.com/teapot123/JASen.
