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....

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Autores principales:	Christopher Ifeanyi Eke, Azah Anir Norman, Liyana Shuib
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4fad5568908a48ac807c253124429372
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spelling	oai:doaj.org-article:4fad5568908a48ac807c2531244293722021-12-02T20:10:48ZMulti-feature fusion framework for sarcasm identification on twitter data: A machine learning based approach.1932-620310.1371/journal.pone.0252918https://doaj.org/article/4fad5568908a48ac807c2531244293722021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0252918https://doaj.org/toc/1932-6203Sarcasm 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.Christopher Ifeanyi EkeAzah Anir NormanLiyana ShuibPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0252918 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Christopher Ifeanyi Eke Azah Anir Norman Liyana Shuib Multi-feature fusion framework for sarcasm identification on twitter data: A machine learning based approach.
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.
format	article
author	Christopher Ifeanyi Eke Azah Anir Norman Liyana Shuib
author_facet	Christopher Ifeanyi Eke Azah Anir Norman Liyana Shuib
author_sort	Christopher Ifeanyi Eke
title	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_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_sort	multi-feature fusion framework for sarcasm identification on twitter data: a machine learning based approach.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/4fad5568908a48ac807c253124429372
work_keys_str_mv	AT christopherifeanyieke multifeaturefusionframeworkforsarcasmidentificationontwitterdataamachinelearningbasedapproach AT azahanirnorman multifeaturefusionframeworkforsarcasmidentificationontwitterdataamachinelearningbasedapproach AT liyanashuib multifeaturefusionframeworkforsarcasmidentificationontwitterdataamachinelearningbasedapproach
_version_	1718374944484622336

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

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