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2019 | OriginalPaper | Buchkapitel

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

verfasst von : Olaf Wallaart, Flavius Frasincar

Erschienen in: The Semantic Web

Verlag: Springer International Publishing

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Abstract

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 (LCR-Rot-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.

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Vorheriges Kapitel Knowledge-Based Short Text Categorization Using Entity and Category Embedding

Nächstes Kapitel Predicting Entity Mentions in Scientific Literature

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Titel: A Hybrid Approach for Aspect-Based Sentiment Analysis Using a Lexicalized Domain Ontology and Attentional Neural Models
verfasst von: Olaf Wallaart
Flavius Frasincar
Verlag: Springer International Publishing
Buch: The Semantic Web
Print ISBN: 978-3-030-21347-3

Electronic ISBN: 978-3-030-21348-0

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-21348-0_24

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