Opinion Transmission Network for Jointly Improving Aspect-oriented Opinion Words Extraction and Sentiment Classification
This work addresses aspect-based sentiment analysis for natural language processing applications, but it is incremental as it builds on existing subtasks by integrating them more effectively.
The paper tackles the joint improvement of aspect-level sentiment classification and aspect-oriented opinion words extraction by proposing the Opinion Transmission Network (OTN), which uses bidirectional opinion transmission mechanisms to facilitate both tasks simultaneously, resulting in outperforming strong baselines on benchmark datasets.
Aspect-level sentiment classification (ALSC) and aspect oriented opinion words extraction (AOWE) are two highly relevant aspect-based sentiment analysis (ABSA) subtasks. They respectively aim to detect the sentiment polarity and extract the corresponding opinion words toward a given aspect in a sentence. Previous works separate them and focus on one of them by training neural models on small-scale labeled data, while neglecting the connections between them. In this paper, we propose a novel joint model, Opinion Transmission Network (OTN), to exploit the potential bridge between ALSC and AOWE to achieve the goal of facilitating them simultaneously. Specifically, we design two tailor-made opinion transmission mechanisms to control opinion clues flow bidirectionally, respectively from ALSC to AOWE and AOWE to ALSC. Experiment results on two benchmark datasets show that our joint model outperforms strong baselines on the two tasks. Further analysis also validates the effectiveness of opinion transmission mechanisms.