A Position Aware Decay Weighted Network for Aspect based Sentiment Analysis
This addresses sentiment analysis for specific aspects in text, but it is incremental as it builds on existing approaches by incorporating positional information more directly.
The paper tackles Aspect Term Sentiment Analysis by proposing a model that uses positional information of aspect terms with a decay mechanism to weight word contributions based on distance from the aspect, achieving effectiveness demonstrated on SemEval 2014 datasets.
Aspect Based Sentiment Analysis (ABSA) is the task of identifying sentiment polarity of a text given another text segment or aspect. In ABSA, a text can have multiple sentiments depending upon each aspect. Aspect Term Sentiment Analysis (ATSA) is a subtask of ABSA, in which aspect terms are contained within the given sentence. Most of the existing approaches proposed for ATSA, incorporate aspect information through a different subnetwork thereby overlooking the advantage of aspect terms' presence within the sentence. In this paper, we propose a model that leverages the positional information of the aspect. The proposed model introduces a decay mechanism based on position. This decay function mandates the contribution of input words for ABSA. The contribution of a word declines as farther it is positioned from the aspect terms in the sentence. The performance is measured on two standard datasets from SemEval 2014 Task 4. In comparison with recent architectures, the effectiveness of the proposed model is demonstrated.