Attention-Based Neural Networks for Sentiment Attitude Extraction using Distant Supervision
This work addresses sentiment analysis for Russian language processing, but it is incremental as it applies existing attention mechanisms to a specific domain and dataset.
The paper tackled sentiment attitude extraction from Russian texts by adapting attention-based context encoders and using distant supervision with an automatically constructed corpus, resulting in a 10% increase in performance and an extra 3% F1 gain for three-class classification models.
In the sentiment attitude extraction task, the aim is to identify <<attitudes>> -- sentiment relations between entities mentioned in text. In this paper, we provide a study on attention-based context encoders in the sentiment attitude extraction task. For this task, we adapt attentive context encoders of two types: (1) feature-based; (2) self-based. In our study, we utilize the corpus of Russian analytical texts RuSentRel and automatically constructed news collection RuAttitudes for enriching the training set. We consider the problem of attitude extraction as two-class (positive, negative) and three-class (positive, negative, neutral) classification tasks for whole documents. Our experiments with the RuSentRel corpus show that the three-class classification models, which employ the RuAttitudes corpus for training, result in 10% increase and extra 3% by F1, when model architectures include the attention mechanism. We also provide the analysis of attention weight distributions in dependence on the term type.