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Multi-feature fusion framework for sarcasm identification on twitter data: A machine learning based approach

Sarcasm is the main reason behind the faulty classification of tweets. It brings a challenge in natural language processing (NLP) as it hampers the method of finding people’s actual sentiment. Various feature engineering techniques are being investigated for the automatic detection of sarcasm. Howev...

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Detalles Bibliográficos
Autores principales: Eke, Christopher Ifeanyi, Norman, Azah Anir, Shuib, Liyana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8191968/
https://www.ncbi.nlm.nih.gov/pubmed/34111192
http://dx.doi.org/10.1371/journal.pone.0252918
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author Eke, Christopher Ifeanyi
Norman, Azah Anir
Shuib, Liyana
author_facet Eke, Christopher Ifeanyi
Norman, Azah Anir
Shuib, Liyana
author_sort Eke, Christopher Ifeanyi
collection PubMed
description Sarcasm is the main reason behind the faulty classification of tweets. It brings a challenge in natural language processing (NLP) as it hampers the method of finding people’s actual sentiment. Various feature engineering techniques are being investigated for the automatic detection of sarcasm. However, most related techniques have always concentrated only on the content-based features in sarcastic expression, leaving the contextual information in isolation. This leads to a loss of the semantics of words in the sarcastic expression. Another drawback is the sparsity of the training data. Due to the word limit of microblog, the feature vector’s values for each sample constructed by BoW produces null features. To address the above-named problems, a Multi-feature Fusion Framework is proposed using two classification stages. The first stage classification is constructed with the lexical feature only, extracted using the BoW technique, and trained using five standard classifiers, including SVM, DT, KNN, LR, and RF, to predict the sarcastic tendency. In stage two, the constructed lexical sarcastic tendency feature is fused with eight other proposed features for modelling a context to obtain a final prediction. The effectiveness of the developed framework is tested with various experimental analysis to obtain classifiers’ performance. The evaluation shows that our constructed classification models based on the developed novel feature fusion obtained results with a precision of 0.947 using a Random Forest classifier. Finally, the obtained results were compared with the results of three baseline approaches. The comparison outcome shows the significance of the proposed framework.
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spelling pubmed-81919682021-06-10 Multi-feature fusion framework for sarcasm identification on twitter data: A machine learning based approach Eke, Christopher Ifeanyi Norman, Azah Anir Shuib, Liyana PLoS One Research Article Sarcasm is the main reason behind the faulty classification of tweets. It brings a challenge in natural language processing (NLP) as it hampers the method of finding people’s actual sentiment. Various feature engineering techniques are being investigated for the automatic detection of sarcasm. However, most related techniques have always concentrated only on the content-based features in sarcastic expression, leaving the contextual information in isolation. This leads to a loss of the semantics of words in the sarcastic expression. Another drawback is the sparsity of the training data. Due to the word limit of microblog, the feature vector’s values for each sample constructed by BoW produces null features. To address the above-named problems, a Multi-feature Fusion Framework is proposed using two classification stages. The first stage classification is constructed with the lexical feature only, extracted using the BoW technique, and trained using five standard classifiers, including SVM, DT, KNN, LR, and RF, to predict the sarcastic tendency. In stage two, the constructed lexical sarcastic tendency feature is fused with eight other proposed features for modelling a context to obtain a final prediction. The effectiveness of the developed framework is tested with various experimental analysis to obtain classifiers’ performance. The evaluation shows that our constructed classification models based on the developed novel feature fusion obtained results with a precision of 0.947 using a Random Forest classifier. Finally, the obtained results were compared with the results of three baseline approaches. The comparison outcome shows the significance of the proposed framework. Public Library of Science 2021-06-10 /pmc/articles/PMC8191968/ /pubmed/34111192 http://dx.doi.org/10.1371/journal.pone.0252918 Text en © 2021 Eke 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, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Eke, Christopher Ifeanyi
Norman, Azah Anir
Shuib, Liyana
Multi-feature fusion framework for sarcasm identification on twitter data: A machine learning based approach
title Multi-feature fusion framework for sarcasm identification on twitter data: A machine learning based approach
title_full Multi-feature fusion framework for sarcasm identification on twitter data: A machine learning based approach
title_fullStr Multi-feature fusion framework for sarcasm identification on twitter data: A machine learning based approach
title_full_unstemmed Multi-feature fusion framework for sarcasm identification on twitter data: A machine learning based approach
title_short Multi-feature fusion framework for sarcasm identification on twitter data: A machine learning based approach
title_sort multi-feature fusion framework for sarcasm identification on twitter data: a machine learning based approach
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8191968/
https://www.ncbi.nlm.nih.gov/pubmed/34111192
http://dx.doi.org/10.1371/journal.pone.0252918
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