A Hybrid Approach for Aspect-Based Sentiment Analysis Using Deep Contextual Word Embeddings and Hierarchical Attention

• URL: http://arxiv.org/abs/2004.08673v1
• Date: Sat, 18 Apr 2020 17:54:55 GMT
• Title: A Hybrid Approach for Aspect-Based Sentiment Analysis Using Deep Contextual Word Embeddings and Hierarchical Attention
• Authors: Maria Mihaela Trusca, Daan Wassenberg, Flavius Frasincar, Rommert Dekker
• Abstract summary: We extend the state-of-the-art Hybrid Approach for Aspect-Based Sentiment Analysis (HAABSA) in two directions. First we replace the non-contextual word embeddings with deep contextual word embeddings in order to better cope with the word semantics in a given text. Second, we use hierarchical attention by adding an extra attention layer to the HAABSA high-level representations in order to increase the method flexibility in modeling the input data.
• Score: 4.742874328556818
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Web has become the main platform where people express their opinions about entities of interest and their associated aspects. Aspect-Based Sentiment Analysis (ABSA) aims to automatically compute the sentiment towards these aspects from opinionated text. In this paper we extend the state-of-the-art Hybrid Approach for Aspect-Based Sentiment Analysis (HAABSA) method in two directions. First we replace the non-contextual word embeddings with deep contextual word embeddings in order to better cope with the word semantics in a given text. Second, we use hierarchical attention by adding an extra attention layer to the HAABSA high-level representations in order to increase the method flexibility in modeling the input data. Using two standard datasets (SemEval 2015 and SemEval 2016) we show that the proposed extensions improve the accuracy of the built model for ABSA.

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