Feature selection based on particle swarm optimization algorithm for sentiment analysis classification
Online media serve as a potential secondary data source for studies on sentiment analysis. The current conditions of the data sources are very different, and it offers a variety of writing systems. Therefore, the results of accuracy in sentiment analysis are very important. An improved approach was...
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| Format: | Conference or Workshop Item |
| Language: | English English |
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IEEE
2021
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/36779/1/Feature%20Selection%20based%20on%20Particle%20Swarm%20Optimization_FULL.pdf http://umpir.ump.edu.my/id/eprint/36779/2/Feature%20selection%20based%20on%20particle%20swarm%20optimization%20.pdf |
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| author | Nurcahyawati, Vivine Mustaffa, Zuriani |
| author_facet | Nurcahyawati, Vivine Mustaffa, Zuriani |
| author_sort | Nurcahyawati, Vivine |
| collection | UMP |
| description | Online media serve as a potential secondary data source for studies on sentiment analysis. The current conditions of the data sources are very different, and it offers a variety of writing systems. Therefore, the results of accuracy in sentiment analysis are very important. An improved approach was proposed to increase the sentiment analysis accuracy based on text pre-processing and Naïve Bayes Classifier algorithm hybrid with Particle Swarm Optimization (NBC-PSO). Furthermore, the proposed algorithm solves the complex background problems about noise data and feature selection that affect the classification performance on sentiment analysis. This proceeded with the classification of positive or negative sentiments on these texts using NBC. Subsequently, the feature selection based on PSO was created to improve the accuracy. The experimental results showed that the proposed approach has a significant effect on sentiment score and polarity detection. |
| first_indexed | 2024-03-06T13:04:05Z |
| format | Conference or Workshop Item |
| id | UMPir36779 |
| institution | Universiti Malaysia Pahang |
| language | English English |
| last_indexed | 2024-03-06T13:04:05Z |
| publishDate | 2021 |
| publisher | IEEE |
| record_format | dspace |
| spelling | UMPir367792023-01-25T01:59:51Z http://umpir.ump.edu.my/id/eprint/36779/ Feature selection based on particle swarm optimization algorithm for sentiment analysis classification Nurcahyawati, Vivine Mustaffa, Zuriani QA76 Computer software TA Engineering (General). Civil engineering (General) Online media serve as a potential secondary data source for studies on sentiment analysis. The current conditions of the data sources are very different, and it offers a variety of writing systems. Therefore, the results of accuracy in sentiment analysis are very important. An improved approach was proposed to increase the sentiment analysis accuracy based on text pre-processing and Naïve Bayes Classifier algorithm hybrid with Particle Swarm Optimization (NBC-PSO). Furthermore, the proposed algorithm solves the complex background problems about noise data and feature selection that affect the classification performance on sentiment analysis. This proceeded with the classification of positive or negative sentiments on these texts using NBC. Subsequently, the feature selection based on PSO was created to improve the accuracy. The experimental results showed that the proposed approach has a significant effect on sentiment score and polarity detection. IEEE 2021 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/36779/1/Feature%20Selection%20based%20on%20Particle%20Swarm%20Optimization_FULL.pdf pdf en http://umpir.ump.edu.my/id/eprint/36779/2/Feature%20selection%20based%20on%20particle%20swarm%20optimization%20.pdf Nurcahyawati, Vivine and Mustaffa, Zuriani (2021) Feature selection based on particle swarm optimization algorithm for sentiment analysis classification. In: International Conference on Intelligent Technology, System and Service for Internet of Everything, ITSS-IoE 20212 , 1 - 2 November 2021 , Virtual, Online. pp. 1-7. (174856). ISBN 978-166543305-1 (Published) https://doi.org/10.1109/ITSS-IoE53029.2021.9615311 |
| spellingShingle | QA76 Computer software TA Engineering (General). Civil engineering (General) Nurcahyawati, Vivine Mustaffa, Zuriani Feature selection based on particle swarm optimization algorithm for sentiment analysis classification |
| title | Feature selection based on particle swarm optimization algorithm for sentiment analysis classification |
| title_full | Feature selection based on particle swarm optimization algorithm for sentiment analysis classification |
| title_fullStr | Feature selection based on particle swarm optimization algorithm for sentiment analysis classification |
| title_full_unstemmed | Feature selection based on particle swarm optimization algorithm for sentiment analysis classification |
| title_short | Feature selection based on particle swarm optimization algorithm for sentiment analysis classification |
| title_sort | feature selection based on particle swarm optimization algorithm for sentiment analysis classification |
| topic | QA76 Computer software TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/36779/1/Feature%20Selection%20based%20on%20Particle%20Swarm%20Optimization_FULL.pdf http://umpir.ump.edu.my/id/eprint/36779/2/Feature%20selection%20based%20on%20particle%20swarm%20optimization%20.pdf |
| work_keys_str_mv | AT nurcahyawativivine featureselectionbasedonparticleswarmoptimizationalgorithmforsentimentanalysisclassification AT mustaffazuriani featureselectionbasedonparticleswarmoptimizationalgorithmforsentimentanalysisclassification |