Exploiting Subjectivity Knowledge Transfer for End-to-End Aspect-Based Sentiment Analysis

Pecar, S.¹, Simko, M.

¹ Faculty of Electrical Engineering and Informatics, Technical University of Kosice, Kosice, Slovakia

While classic aspect-based sentiment analysis typically includes three sub-tasks (aspect extraction, opinion extraction, and aspect-level sentiment classification), recent studies focus on exploring possibilities of knowledge sharing from different tasks, such as document-level sentiment analysis or document-level domain classification that are less demanding on dataset resources. Several recent studies managed to propose different frameworks for solving nearly complete end-to-end aspect-based sentiment analysis in a unified manner. However, none of them studied the possibility of transferring knowledge about their subjectivity or opinion typology between sub-tasks. In this work, we propose subjectivity-aware learning as a novel auxiliary task for aspect-based sentiment analysis. Besides, we also propose another novel task defined as opinion type detection. We performed extensive experiments on the state-of-the-art dataset that show improvement of model performance while employing subjectivity learning. All models report improvement in overall F1 score for aspect-based sentiment analysis. In addition, we also set new benchmark results for the separate task of subjectivity detection and opinion type detection for the restaurant domain of SemEval 2015 dataset.

Cite: Pecar, S., Simko, M. Exploiting Subjectivity Knowledge Transfer for End-to-End Aspect-Based Sentiment Analysis. International Conference on Text, Speech, and Dialogue (2021). DOI: 10.1007/978-3-030-83527-9_23