Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis
Wenya Wang, Sinno Jialin Pan, Daniel Dahlmeier, Xiaokui Xiao · 2016
In aspect-based sentiment analysis, extracting aspect terms along with the opinions being expressed from user-generated content is one of the most important subtasks.Previous studies have shown that exploiting connections between aspect and opinion terms is promising for this task.In this paper, we propose a novel joint model that integrates recursive neural networks and conditional random fields into a unified framework for explicit aspect and opinion terms co-extraction.The proposed model learns high-level discriminative features and double propagates information between aspect and opinion terms, simultaneously.Moreover, it is flexible to incorporate hand-crafted features into the proposed model to further boost its information extraction performance.Experimental results on the dataset from SemEval Challenge 2014 task 4 show the superiority of our proposed model over several baseline methods as well as the winning systems of the challenge.