research-article

Gender-based multi-aspect sentiment detection using multilabel learning

Authors:

J. Ashok Kumar,

Tina Esther Trueman,

Erik CambriaAuthors Info & Claims

Volume 606, Issue C

Pages 453 - 468

https://doi.org/10.1016/j.ins.2022.05.057

Published: 01 August 2022 Publication History

Highlights

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Identifies multi-aspect sentiment based on genders in online drug reviews.

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Explores different feature representations such as BoW, TFIDF, and GloVe.

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Compares the problem transformation approach, adapted algorithms, and attention-based recurrent neural networks (RNN).

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Evaluates all model based on samples, labels, and ranks measures.

Abstract

Sentiment analysis is an important task in the field of natural language processing that aims to gauge and predict people’s opinions from large amounts of data. In particular, gender-based sentiment analysis can influence stakeholders and drug developers in real-world markets. In this work, we present a gender-based multi-aspect sentiment detection model using multilabel learning algorithms. We divide Abilify and Celebrex datasets into three groups based on gender information, namely: male, female, and mixed. We then represent bag-of-words (BoW), term frequency-inverse document frequency (TF-IDF), and global vectors for word representation (GloVe) based features for each group. Next, we apply problem transformation approaches and multichannel recurrent neural networks with attention mechanism. Results show that traditional multilabel transformation methods achieve better performance for small amounts of data and long-range sequence in terms of samples and labels, and that deep learning models achieve better performance in terms of mean test accuracy, AUC Score, RL, and average precision using GloVe word embedding features in both datasets.

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Cited By

Chauhan ASharma AMohana R(2024)A Pre-Trained Model for Aspect-based Sentiment Analysis TaskProcedia Computer Science10.1016/j.procs.2024.03.193233:C(35-44)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.procs.2024.03.193
Wu YYin HZhou QLiu DWei DDong J(2024)Multi-hop community question answering based on multi-aspect heterogeneous graphInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10354361:1Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.ipm.2023.103543
Hussain FWasim MCheema SPires I(2024)Semantic features analysis for biomedical lexical answer type prediction using ensemble learning approachKnowledge and Information Systems10.1007/s10115-024-02113-766:8(5003-5019)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1007/s10115-024-02113-7
Show More Cited By

Index Terms

Gender-based multi-aspect sentiment detection using multilabel learning
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches
      1. Neural networks
2. Information systems

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 606, Issue C

Aug 2022

984 pages

ISSN:0020-0255

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Elsevier Inc.

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Elsevier Science Inc.

United States

Publication History

Published: 01 August 2022

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Cited By

Chauhan ASharma AMohana R(2024)A Pre-Trained Model for Aspect-based Sentiment Analysis TaskProcedia Computer Science10.1016/j.procs.2024.03.193233:C(35-44)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.procs.2024.03.193
Wu YYin HZhou QLiu DWei DDong J(2024)Multi-hop community question answering based on multi-aspect heterogeneous graphInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10354361:1Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.ipm.2023.103543
Hussain FWasim MCheema SPires I(2024)Semantic features analysis for biomedical lexical answer type prediction using ensemble learning approachKnowledge and Information Systems10.1007/s10115-024-02113-766:8(5003-5019)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1007/s10115-024-02113-7
Chauhan GNahta RMeena YGopalani D(2023)Aspect based sentiment analysis using deep learning approachesComputer Science Review10.1016/j.cosrev.2023.10057649:COnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.cosrev.2023.100576
Ho SWang ZQuek BCambria E(2023)Knowledge Representation for Conceptual, Motivational, and Affective Processes in Natural Language CommunicationAdvances in Brain Inspired Cognitive Systems10.1007/978-981-97-1417-9_2(14-30)Online publication date: 5-Aug-2023
https://dl.acm.org/doi/10.1007/978-981-97-1417-9_2
Chen LWang YLi H(2022)Enhancement of DNN-based multilabel classification by grouping labels based on data imbalance and label correlationPattern Recognition10.1016/j.patcog.2022.108964132:COnline publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1016/j.patcog.2022.108964

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