Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163883
Title: Gender-based multi-aspect sentiment detection using multilabel learning
Authors: Kumar, J. Ashok
Trueman, Tina Esther
Cambria, Erik
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Kumar, J. A., Trueman, T. E. & Cambria, E. (2022). Gender-based multi-aspect sentiment detection using multilabel learning. Information Sciences, 606, 453-468. https://dx.doi.org/10.1016/j.ins.2022.05.057
Journal: Information Sciences
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.
URI: https://hdl.handle.net/10356/163883
ISSN: 0020-0255
DOI: 10.1016/j.ins.2022.05.057
Rights: © 2022 Elsevier Inc. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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