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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8191968/?tool=EBI |
| _version_ | 1819065546341089280 |
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| author | Christopher Ifeanyi Eke Azah Anir Norman Liyana Shuib |
| author_facet | Christopher Ifeanyi Eke Azah Anir Norman Liyana Shuib |
| author_sort | Christopher Ifeanyi Eke |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-21T15:48:11Z |
| format | Article |
| id | doaj.art-f8f17cd3c62c4112af7ae7530f806a1f |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-21T15:48:11Z |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-f8f17cd3c62c4112af7ae7530f806a1f2022-12-21T18:58:19ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01166Multi-feature fusion framework for sarcasm identification on twitter data: A machine learning based approachChristopher Ifeanyi EkeAzah Anir NormanLiyana ShuibSarcasm 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.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8191968/?tool=EBI |
| spellingShingle | Christopher Ifeanyi Eke Azah Anir Norman Liyana Shuib Multi-feature fusion framework for sarcasm identification on twitter data: A machine learning based approach PLoS ONE |
| 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 |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8191968/?tool=EBI |
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