Intelligent Hybrid Feature Selection for Textual Sentiment Classification
<p dir="ltr">Sentiment Analysis (SA) aims to extract useful information from online Unstructured User-Generated Contents (UUGC) and classify them into positive and negative classes. State-of-the-art techniques for SA suffer a high dimensional feature space because of noisy and irrele...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , |
| منشور في: |
2021
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| الموضوعات: | |
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| _version_ | 1864513505725513728 |
|---|---|
| author | Jawad Khan (6422669) |
| author2 | Aftab Alam (5158601) Youngmoon Lee (19570210) |
| author2_role | author author |
| author_facet | Jawad Khan (6422669) Aftab Alam (5158601) Youngmoon Lee (19570210) |
| author_role | author |
| dc.creator.none.fl_str_mv | Jawad Khan (6422669) Aftab Alam (5158601) Youngmoon Lee (19570210) |
| dc.date.none.fl_str_mv | 2021-10-08T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2021.3118982 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Intelligent_Hybrid_Feature_Selection_for_Textual_Sentiment_Classification/26976598 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Machine learning Feature extraction Support vector machines Motion pictures Entropy Sentiment analysis Semantics Social networking (online) Sentiment classification hybrid feature selection ensemble learning linguistic semantic rules wide coverage sentiment lexicons natural language processing |
| dc.title.none.fl_str_mv | Intelligent Hybrid Feature Selection for Textual Sentiment Classification |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Sentiment Analysis (SA) aims to extract useful information from online Unstructured User-Generated Contents (UUGC) and classify them into positive and negative classes. State-of-the-art techniques for SA suffer a high dimensional feature space because of noisy and irrelevant features from the UUGC. Researchers have also proposed feature extraction and selection techniques to reduce high dimensional feature space, but they fall short in extracting and selecting the most effective sentiment features for sentiment model learning. Effective feature extraction and selection are significant for the SA because they can boost the learning algorithm’s predictive performance while reducing the high-dimensional feature space. To address these concerns, we propose an Intelligent Hybrid Feature Selection for Sentiment Analysis (IHFSSA) based on ensemble learning methods. IHFSSA first identifies sentiment features in the review text utilizing Penn Treebank part-of-speech tagset and integrated Wide Coverage Sentiment Lexicons (WCSL). The sentiment features subset is then selected employing a fast and simple rank-based ensemble of multiple filters feature selection method. The selected sentiment features are further refined by applying a wrapper-based backward feature selection method. Finally, for textual sentiment classification, the well-known classification algorithms Support Vector Machine (SVM), Naive Bayes (NB), Generalized Linear Model (GLM) are trained in the ensemble model on the refined sentiment feature set. The in-depth evaluation using heterogeneous domain benchmark datasets demonstrates that IHFSSA outperforms existing SA techniques.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" rel="noreferrer" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2021.3118982" target="_blank">https://dx.doi.org/10.1109/access.2021.3118982</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_f1d875b2a495cd2b59f37ac7cbef30ff |
| identifier_str_mv | 10.1109/access.2021.3118982 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26976598 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Intelligent Hybrid Feature Selection for Textual Sentiment ClassificationJawad Khan (6422669)Aftab Alam (5158601)Youngmoon Lee (19570210)Information and computing sciencesMachine learningFeature extractionSupport vector machinesMotion picturesEntropySentiment analysisSemanticsSocial networking (online)Sentiment classificationhybrid feature selectionensemble learninglinguistic semantic ruleswide coverage sentiment lexiconsnatural language processing<p dir="ltr">Sentiment Analysis (SA) aims to extract useful information from online Unstructured User-Generated Contents (UUGC) and classify them into positive and negative classes. State-of-the-art techniques for SA suffer a high dimensional feature space because of noisy and irrelevant features from the UUGC. Researchers have also proposed feature extraction and selection techniques to reduce high dimensional feature space, but they fall short in extracting and selecting the most effective sentiment features for sentiment model learning. Effective feature extraction and selection are significant for the SA because they can boost the learning algorithm’s predictive performance while reducing the high-dimensional feature space. To address these concerns, we propose an Intelligent Hybrid Feature Selection for Sentiment Analysis (IHFSSA) based on ensemble learning methods. IHFSSA first identifies sentiment features in the review text utilizing Penn Treebank part-of-speech tagset and integrated Wide Coverage Sentiment Lexicons (WCSL). The sentiment features subset is then selected employing a fast and simple rank-based ensemble of multiple filters feature selection method. The selected sentiment features are further refined by applying a wrapper-based backward feature selection method. Finally, for textual sentiment classification, the well-known classification algorithms Support Vector Machine (SVM), Naive Bayes (NB), Generalized Linear Model (GLM) are trained in the ensemble model on the refined sentiment feature set. The in-depth evaluation using heterogeneous domain benchmark datasets demonstrates that IHFSSA outperforms existing SA techniques.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" rel="noreferrer" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2021.3118982" target="_blank">https://dx.doi.org/10.1109/access.2021.3118982</a></p>2021-10-08T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2021.3118982https://figshare.com/articles/journal_contribution/Intelligent_Hybrid_Feature_Selection_for_Textual_Sentiment_Classification/26976598CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/269765982021-10-08T09:00:00Z |
| spellingShingle | Intelligent Hybrid Feature Selection for Textual Sentiment Classification Jawad Khan (6422669) Information and computing sciences Machine learning Feature extraction Support vector machines Motion pictures Entropy Sentiment analysis Semantics Social networking (online) Sentiment classification hybrid feature selection ensemble learning linguistic semantic rules wide coverage sentiment lexicons natural language processing |
| status_str | publishedVersion |
| title | Intelligent Hybrid Feature Selection for Textual Sentiment Classification |
| title_full | Intelligent Hybrid Feature Selection for Textual Sentiment Classification |
| title_fullStr | Intelligent Hybrid Feature Selection for Textual Sentiment Classification |
| title_full_unstemmed | Intelligent Hybrid Feature Selection for Textual Sentiment Classification |
| title_short | Intelligent Hybrid Feature Selection for Textual Sentiment Classification |
| title_sort | Intelligent Hybrid Feature Selection for Textual Sentiment Classification |
| topic | Information and computing sciences Machine learning Feature extraction Support vector machines Motion pictures Entropy Sentiment analysis Semantics Social networking (online) Sentiment classification hybrid feature selection ensemble learning linguistic semantic rules wide coverage sentiment lexicons natural language processing |