SBERTiment: A New Pipeline to Solve Aspect Based Sentiment Analysis in the Zero-Shot Setting

Abstract

The field of Natural Language Processing is gaining increased attention for the Aspect Based Sentiment Analysis task due to its ability to provide fine-grained information. This paper introduces SBERTiment, a novel approach to perform Aspect Based Sentiment Analysis. The method extracts relevant topics along with their sentiments from the input text by using a 2-step pipeline. In the first step, a token classification model is used to identify the relevant aspect terms and their sentiments. In the second step, a Sentence-BERT embedding model maps each aspect term to a predefined aspect category. Our approach has been tested on benchmark datasets and has achieved scores that are comparable to the best-performing methods. The pipeline is also able to perform zero-shot classification, which means it can extract information in unseen domains without additional training. When evaluated on a dataset with unseen aspect categories, SBERTiment achieved the best score among benchmark approaches.