A Hybrid Approach for Aspect-Based Sentiment Analysis Using a Lexicalized Domain Ontology and Attentional Neural Models

ESWC 2019  ·  Olaf Wallaart; Flavius Frasincar ·

This work focuses on sentence-level aspect-based sentiment analysis for restaurant reviews. A two-stage sentiment analysis algorithm is proposed. In this method, first a lexicalized domain ontology is used to predict the sentiment and as a back-up algorithm a neural network with a rotatory attention mechanism (LCR-Rot) is utilized. Furthermore, two features are added to the backup algorithm. The first extension changes the order in which the rotatory attention mechanism operates (LCRRot-inv). The second extension runs over the rotatory attention mechanism for multiple iterations (LCR-Rot-hop). Using the SemEval-2015 and SemEval-2016 data, we conclude that the two-stage method outperforms the baseline methods, albeit with a small percentage. Moreover, we find that the method where we iterate multiple times over a rotatory attention mechanism has the best performance.



Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Aspect-Based Sentiment Analysis (ABSA) SemEval 2015 Task 12 HAABSA Restaurant (Acc) 80.6 # 1
Aspect-Based Sentiment Analysis (ABSA) SemEval-2016 Task 5 Subtask 1 HAABSA Restaurant (Acc) 88.0 # 2


No methods listed for this paper. Add relevant methods here