research-article

Exploring lexico-semantic patterns for aspect-based sentiment analysis

Authors:

Frederique Baas,

Alexander Osinga,

Nikki van de Ven,

Steffie van Loenhout,

Lisanne Vrolijk,

Flavius FrasincarAuthors Info & Claims

SAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

Pages 984 - 992

https://doi.org/10.1145/3297280.3297377

Published: 08 April 2019 Publication History

Abstract

Web 2.0 has caused a boom in user-generated content, which contains a lot of valuable information. Analysis of these natural language data requires advanced machine learning techniques. This research focuses on determining aspect-based sentiment in consumer reviews using lexico-semantic patterns. We propose a method using a Support Vector Machine with 6 different pattern classes: lexical, syntactical, semantic, sentiment, hybrid, and surface. We show that several of these patterns, including synset bigram, negator-POS bigram, and POS bigram, can be used to better determine the aspect-based sentiment, using two widely used real-world data sets on consumer reviews. Our approach achieves 69.0% and 73.1% F₁ score for the two data sets, respectively, an increase of 15.3% and 16.1% respectively compared to the considered baseline.

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Cited By

Jorvekar GGangwar M(2022)Multi-entity Topic Modeling and Aspect-Based Sentiment Classification Using Machine Learning ApproachProceedings of International Conference on Recent Trends in Computing10.1007/978-981-16-7118-0_46(537-547)Online publication date: 15-Jan-2022
https://doi.org/10.1007/978-981-16-7118-0_46

Index Terms

Exploring lexico-semantic patterns for aspect-based sentiment analysis
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Information extraction
      2. Sentiment analysis
  2. World Wide Web
    1. Web mining

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Abstract
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Graphical abstract

Display Omitted
Highlights
- We propose DRD-SE-GCN, a novel sentiment analysis model that enhances long and short sentences.
- For long sentences, we refine the graph to retain sentiment info from distant words.
- For short sentences, we adjust the graph edges ...

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cover image ACM Conferences

SAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

April 2019

2682 pages

ISBN:9781450359337

DOI:10.1145/3297280

Conference Chairs:
Chih-Cheng Hung
Kennesaw State University, Marietta, Georgia
,
George A. Papadopoulos
University of Cyprus, Nicosia, Cyprus

Copyright © 2019 ACM.

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Published: 08 April 2019

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SAC '19: The 34th ACM/SIGAPP Symposium on Applied Computing

April 8 - 12, 2019

Limassol, Cyprus

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

Jorvekar GGangwar M(2022)Multi-entity Topic Modeling and Aspect-Based Sentiment Classification Using Machine Learning ApproachProceedings of International Conference on Recent Trends in Computing10.1007/978-981-16-7118-0_46(537-547)Online publication date: 15-Jan-2022
https://doi.org/10.1007/978-981-16-7118-0_46

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