Title: A hybrid approach for aspect-based sentiment analysis using a double rotatory attention model
Authors: Guangyao Zhou; Jingyi Cheng; Flavius Frasincar
Addresses: Econometric Institute, Erasmus University Rotterdam, P.O. Box 1738, 3000 DR Rotterdam, The Netherlands ' Econometric Institute, Erasmus University Rotterdam, P.O. Box 1738, 3000 DR Rotterdam, The Netherlands ' Econometric Institute, Erasmus University Rotterdam, P.O. Box 1738, 3000 DR Rotterdam, The Netherlands
Abstract: Nowadays, the web is an essential hub for gathering comments on entities and their associated aspects. In this paper, we propose a model which is capable of extracting these opinions and predicting the sentiment scores in aspect-level sentiment mining. In our two-step approach, a lexicalised domain ontology is firstly applied for sentiment classification. If the result is inconclusive from the first step, the backup model double rotatory attention mechanism is applied, which utilises deep contextual word embeddings to better capture the (multi-)word semantics in the given text. This study contributes to the current research by introducing novel repetition and rotatory structures to refine the attention mechanism. It is shown that our model outperforms state-of-the-art methods on the datasets of SemEval 2015 and SemEval 2016.
Keywords: LCR-Rot; double rotatory attention; DRA; contextual word embeddings; BERT; sentiment analysis; classification; lexicalised domain ontology; hierarchical attention; hybrid approach.
International Journal of Web Engineering and Technology, 2022 Vol.17 No.1, pp.3 - 28
Received: 10 Sep 2021
Accepted: 28 Feb 2022
Published online: 25 Aug 2022 *