Active learning enhanced semi-automatic annotation tool for aspect-based sentiment analysis

A semi-automatic annotation tool which uses active learning to increase the effectiveness of the documents annotation and applied it in the domain of hotels evaluations, confirming the faster increase of the annotation suggestions quality in terms of F1-measure in comparison to the scenario without active learning.

Abstract

Aspect-based sentiment analysis has become popular research field which allows the quantification of textual evaluations of different aspects of products and services. Methods of aspect-based sentiment analysis built on machine learning usually depend on manually annotated training corpora. In order to facilitate the processes of their creation, annotation tools dedicated to this purpose are needed. In this work we proposed a semi-automatic annotation tool which uses active learning to increase the effectiveness of the documents annotation. The use of active learning adapted to the needs of aspect-based sentiment analysis is the main difference between the proposed solution and existing annotation tools. We applied it in the domain of hotels evaluations. The results of realized experiments confirmed the faster increase of the annotation suggestions quality in terms of F1-measure in comparison to the scenario without active learning.