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Semantic relational machine learning model for sentiment analysis using cascade feature selection and heterogeneous classifier ensemble
The exponential rise in social media via microblogging sites like Twitter has sparked curiosity in sentiment analysis that exploits user feedback towards a targeted product or service. Considering its significance in business intelligence and decision-making, numerous efforts have been made in this...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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PeerJ Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575864/ https://www.ncbi.nlm.nih.gov/pubmed/36262147 http://dx.doi.org/10.7717/peerj-cs.1100 |
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author | Yenkikar, Anuradha Babu, C. Narendra Hemanth, D. Jude |
author_facet | Yenkikar, Anuradha Babu, C. Narendra Hemanth, D. Jude |
author_sort | Yenkikar, Anuradha |
collection | PubMed |
description | The exponential rise in social media via microblogging sites like Twitter has sparked curiosity in sentiment analysis that exploits user feedback towards a targeted product or service. Considering its significance in business intelligence and decision-making, numerous efforts have been made in this area. However, lack of dictionaries, unannotated data, large-scale unstructured data, and low accuracies have plagued these approaches. Also, sentiment classification through classifier ensemble has been underexplored in literature. In this article, we propose a Semantic Relational Machine Learning (SRML) model that automatically classifies the sentiment of tweets by using classifier ensemble and optimal features. The model employs the Cascaded Feature Selection (CFS) strategy, a novel statistical assessment approach based on Wilcoxon rank sum test, univariate logistic regression assisted significant predictor test and cross-correlation test. It further uses the efficacy of word2vec-based continuous bag-of-words and n-gram feature extraction in conjunction with SentiWordNet for finding optimal features for classification. We experiment on six public Twitter sentiment datasets, the STS-Gold dataset, the Obama-McCain Debate (OMD) dataset, the healthcare reform (HCR) dataset and the SemEval2017 Task 4A, 4B and 4C on a heterogeneous classifier ensemble comprising fourteen individual classifiers from different paradigms. Results from the experimental study indicate that CFS supports in attaining a higher classification accuracy with up to 50% lesser features compared to count vectorizer approach. In Intra-model performance assessment, the Artificial Neural Network-Gradient Descent (ANN-GD) classifier performs comparatively better than other individual classifiers, but the Best Trained Ensemble (BTE) strategy outperforms on all metrics. In inter-model performance assessment with existing state-of-the-art systems, the proposed model achieved higher accuracy and outperforms more accomplished models employing quantum-inspired sentiment representation (QSR), transformer-based methods like BERT, BERTweet, RoBERTa and ensemble techniques. The research thus provides critical insights into implementing similar strategy into building more generic and robust expert system for sentiment analysis that can be leveraged across industries. |
format | Online Article Text |
id | pubmed-9575864 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95758642022-10-18 Semantic relational machine learning model for sentiment analysis using cascade feature selection and heterogeneous classifier ensemble Yenkikar, Anuradha Babu, C. Narendra Hemanth, D. Jude PeerJ Comput Sci Artificial Intelligence The exponential rise in social media via microblogging sites like Twitter has sparked curiosity in sentiment analysis that exploits user feedback towards a targeted product or service. Considering its significance in business intelligence and decision-making, numerous efforts have been made in this area. However, lack of dictionaries, unannotated data, large-scale unstructured data, and low accuracies have plagued these approaches. Also, sentiment classification through classifier ensemble has been underexplored in literature. In this article, we propose a Semantic Relational Machine Learning (SRML) model that automatically classifies the sentiment of tweets by using classifier ensemble and optimal features. The model employs the Cascaded Feature Selection (CFS) strategy, a novel statistical assessment approach based on Wilcoxon rank sum test, univariate logistic regression assisted significant predictor test and cross-correlation test. It further uses the efficacy of word2vec-based continuous bag-of-words and n-gram feature extraction in conjunction with SentiWordNet for finding optimal features for classification. We experiment on six public Twitter sentiment datasets, the STS-Gold dataset, the Obama-McCain Debate (OMD) dataset, the healthcare reform (HCR) dataset and the SemEval2017 Task 4A, 4B and 4C on a heterogeneous classifier ensemble comprising fourteen individual classifiers from different paradigms. Results from the experimental study indicate that CFS supports in attaining a higher classification accuracy with up to 50% lesser features compared to count vectorizer approach. In Intra-model performance assessment, the Artificial Neural Network-Gradient Descent (ANN-GD) classifier performs comparatively better than other individual classifiers, but the Best Trained Ensemble (BTE) strategy outperforms on all metrics. In inter-model performance assessment with existing state-of-the-art systems, the proposed model achieved higher accuracy and outperforms more accomplished models employing quantum-inspired sentiment representation (QSR), transformer-based methods like BERT, BERTweet, RoBERTa and ensemble techniques. The research thus provides critical insights into implementing similar strategy into building more generic and robust expert system for sentiment analysis that can be leveraged across industries. PeerJ Inc. 2022-09-20 /pmc/articles/PMC9575864/ /pubmed/36262147 http://dx.doi.org/10.7717/peerj-cs.1100 Text en © 2022 Yenkikar et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Artificial Intelligence Yenkikar, Anuradha Babu, C. Narendra Hemanth, D. Jude Semantic relational machine learning model for sentiment analysis using cascade feature selection and heterogeneous classifier ensemble |
title | Semantic relational machine learning model for sentiment analysis using cascade feature selection and heterogeneous classifier ensemble |
title_full | Semantic relational machine learning model for sentiment analysis using cascade feature selection and heterogeneous classifier ensemble |
title_fullStr | Semantic relational machine learning model for sentiment analysis using cascade feature selection and heterogeneous classifier ensemble |
title_full_unstemmed | Semantic relational machine learning model for sentiment analysis using cascade feature selection and heterogeneous classifier ensemble |
title_short | Semantic relational machine learning model for sentiment analysis using cascade feature selection and heterogeneous classifier ensemble |
title_sort | semantic relational machine learning model for sentiment analysis using cascade feature selection and heterogeneous classifier ensemble |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575864/ https://www.ncbi.nlm.nih.gov/pubmed/36262147 http://dx.doi.org/10.7717/peerj-cs.1100 |
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