Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis

Understand

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 propagate information between aspect and opinion terms, simultaneously.