EliXa: A Modular and Flexible ABSA Platform
It addresses ABSA for researchers by offering a flexible experimental tool, but it is incremental as it uses existing methods like sequence labeling and SVM.
The paper tackles Aspect Based Sentiment Analysis (ABSA) by developing a modular platform, achieving best results in Opinion Target Extraction and competitive accuracies (e.g., 0.70-0.80) in target polarity classification across domains.
This paper presents a supervised Aspect Based Sentiment Analysis (ABSA) system. Our aim is to develop a modular platform which allows to easily conduct experiments by replacing the modules or adding new features. We obtain the best result in the Opinion Target Extraction (OTE) task (slot 2) using an off-the-shelf sequence labeler. The target polarity classification (slot 3) is addressed by means of a multiclass SVM algorithm which includes lexical based features such as the polarity values obtained from domain and open polarity lexicons. The system obtains accuracies of 0.70 and 0.73 for the restaurant and laptop domain respectively, and performs second best in the out-of-domain hotel, achieving an accuracy of 0.80.