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Performance evaluation of hybrid feature selection technique for sentiment classification based on food reviews

This paper presents an evaluation of the performance efficiency of sentiment classification using a hybrid feature selection technique. This technique is able to overcome the issue of lack in evaluating features importance by using a combination of TF-IDF+SVM-RFE (Term Frequency-Inverse Document Fre...

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主要な著者: Suryanti, Awang, Nur Syafiqah, Mohd Nafis
フォーマット: Conference or Workshop Item
言語:English
English
出版事項: IEEE 2021
主題:
Q Science (General)
QA76 Computer software
オンライン･アクセス:http://umpir.ump.edu.my/id/eprint/33473/1/Performance%20evaluation%20of%20hybrid%20feature%20selection%20technique_FULL.pdf
http://umpir.ump.edu.my/id/eprint/33473/2/Performance%20evaluation%20of%20hybrid%20feature%20selection%20technique.pdf
http://umpir.ump.edu.my/id/eprint/33473/
https://doi.org/10.1109/ICSECS52883.2021.00038
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spelling my.ump.umpir.334732024-01-08T01:41:34Z http://umpir.ump.edu.my/id/eprint/33473/ Performance evaluation of hybrid feature selection technique for sentiment classification based on food reviews Suryanti, Awang Nur Syafiqah, Mohd Nafis Q Science (General) QA76 Computer software This paper presents an evaluation of the performance efficiency of sentiment classification using a hybrid feature selection technique. This technique is able to overcome the issue of lack in evaluating features importance by using a combination of TF-IDF+SVM-RFE (Term Frequency-Inverse Document Frequency (TF-IDF) and Supports Vector Machine (SVM-RFE)). Feature importance is measured and significant features are selected recursively based on the number of significant features known as k-top features. We tested this technique with a food reviews dataset from Kaggle to classify a positive and negative review. Finally, SVM has been deployed as a classifier to evaluate the classification performance. The performance is observed based on the accuracy, precision, recall and F-measure. The highest accuracy is 80%, precision is 82%, recall is 76% and F-measure is 79%. Consequently, 24.5% of the features to be classified in this technique have been reduced in obtaining these highest results. Thus, the computational resources are able to be utilized optimally from this reduction and the classification performance efficiency is able to be maintained. IEEE 2021 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/33473/1/Performance%20evaluation%20of%20hybrid%20feature%20selection%20technique_FULL.pdf pdf en http://umpir.ump.edu.my/id/eprint/33473/2/Performance%20evaluation%20of%20hybrid%20feature%20selection%20technique.pdf Suryanti, Awang and Nur Syafiqah, Mohd Nafis (2021) Performance evaluation of hybrid feature selection technique for sentiment classification based on food reviews. In: 7th International Conference on Software Engineering and Computer Systems and 4th International Conference on Computational Science and Information Management, ICSECS-ICOCSIM 2021 , 24 - 26 Aug. 2021 , Pekan, Malaysia. 172 -176. (171807). ISBN 978-166541407-4 https://doi.org/10.1109/ICSECS52883.2021.00038
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic Q Science (General)
QA76 Computer software
spellingShingle Q Science (General)
QA76 Computer software
Suryanti, Awang
Nur Syafiqah, Mohd Nafis
Performance evaluation of hybrid feature selection technique for sentiment classification based on food reviews
description This paper presents an evaluation of the performance efficiency of sentiment classification using a hybrid feature selection technique. This technique is able to overcome the issue of lack in evaluating features importance by using a combination of TF-IDF+SVM-RFE (Term Frequency-Inverse Document Frequency (TF-IDF) and Supports Vector Machine (SVM-RFE)). Feature importance is measured and significant features are selected recursively based on the number of significant features known as k-top features. We tested this technique with a food reviews dataset from Kaggle to classify a positive and negative review. Finally, SVM has been deployed as a classifier to evaluate the classification performance. The performance is observed based on the accuracy, precision, recall and F-measure. The highest accuracy is 80%, precision is 82%, recall is 76% and F-measure is 79%. Consequently, 24.5% of the features to be classified in this technique have been reduced in obtaining these highest results. Thus, the computational resources are able to be utilized optimally from this reduction and the classification performance efficiency is able to be maintained.
format Conference or Workshop Item
author Suryanti, Awang
Nur Syafiqah, Mohd Nafis
author_facet Suryanti, Awang
Nur Syafiqah, Mohd Nafis
author_sort Suryanti, Awang
title Performance evaluation of hybrid feature selection technique for sentiment classification based on food reviews
title_short Performance evaluation of hybrid feature selection technique for sentiment classification based on food reviews
title_full Performance evaluation of hybrid feature selection technique for sentiment classification based on food reviews
title_fullStr Performance evaluation of hybrid feature selection technique for sentiment classification based on food reviews
title_full_unstemmed Performance evaluation of hybrid feature selection technique for sentiment classification based on food reviews
title_sort performance evaluation of hybrid feature selection technique for sentiment classification based on food reviews
publisher IEEE
publishDate 2021
url http://umpir.ump.edu.my/id/eprint/33473/1/Performance%20evaluation%20of%20hybrid%20feature%20selection%20technique_FULL.pdf
http://umpir.ump.edu.my/id/eprint/33473/2/Performance%20evaluation%20of%20hybrid%20feature%20selection%20technique.pdf
http://umpir.ump.edu.my/id/eprint/33473/
https://doi.org/10.1109/ICSECS52883.2021.00038
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score 13.251813

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