Analysis of Feature Selection Methods for Sentiment Analysis Concerning Covid-19 Vaccination Issues
Sentiment analysis or opinion mining is a computational study of a person's opinions, sentiments, evaluations, attitudes, moods, and emotions. Sentiment analysis is one of the most active research areas in natural language processing, data mining, information retrieval, and web mining. One of...
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| Format: | Article |
| Language: | English |
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INTI International University
2023
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| Online Access: | http://eprints.intimal.edu.my/1729/ http://eprints.intimal.edu.my/1729/1/jods2023_03.pdf |
| _version_ | 1848766818266644480 |
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| author | Muhammad, Fajar Tri Basuki, Kurniawan Edi Surya, Negara Harahap |
| author_facet | Muhammad, Fajar Tri Basuki, Kurniawan Edi Surya, Negara Harahap |
| author_sort | Muhammad, Fajar |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | Sentiment analysis or opinion mining is a computational study of a person's opinions, sentiments,
evaluations, attitudes, moods, and emotions. Sentiment analysis is one of the most active research
areas in natural language processing, data mining, information retrieval, and web mining. One of
the problems identified in the sentiment analysis process is the massive amount of data or text
properties. In sentiment analysis, each word or term is collected into properties or dimensions,
forming a data table. Due to the vast number of terms, this causes the process to take too long and
requires a computer with tremendous power or ability. In addition, this can lead to a decrease in
the quality of the model because data that is too large will also provide a significant bias value.
Not all terms have contributions or relationships to decisions or labels in the form of positive,
negative, and neutral values. For this reason, the feature selection method will be used in this study
to select features or terms that contribute more to decisions or labels. It is also hoped that this can
increase the quality of the prediction model that will be formed. In this study, the author will
continue the research from another researcher by adding a feature selection process, such as two
algorithms from the filtered method, chi-square, and information gain, and one algorithm from the
wrapped method, which is Genetic Algorithms (GA). The experiment result shows that the GA
obtained result has the highest accurate value compared to the other methods. |
| first_indexed | 2025-11-14T11:57:11Z |
| format | Article |
| id | intimal-1729 |
| institution | INTI International University |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:57:11Z |
| publishDate | 2023 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-17292023-07-13T08:29:56Z http://eprints.intimal.edu.my/1729/ Analysis of Feature Selection Methods for Sentiment Analysis Concerning Covid-19 Vaccination Issues Muhammad, Fajar Tri Basuki, Kurniawan Edi Surya, Negara Harahap QA75 Electronic computers. Computer science QA76 Computer software Sentiment analysis or opinion mining is a computational study of a person's opinions, sentiments, evaluations, attitudes, moods, and emotions. Sentiment analysis is one of the most active research areas in natural language processing, data mining, information retrieval, and web mining. One of the problems identified in the sentiment analysis process is the massive amount of data or text properties. In sentiment analysis, each word or term is collected into properties or dimensions, forming a data table. Due to the vast number of terms, this causes the process to take too long and requires a computer with tremendous power or ability. In addition, this can lead to a decrease in the quality of the model because data that is too large will also provide a significant bias value. Not all terms have contributions or relationships to decisions or labels in the form of positive, negative, and neutral values. For this reason, the feature selection method will be used in this study to select features or terms that contribute more to decisions or labels. It is also hoped that this can increase the quality of the prediction model that will be formed. In this study, the author will continue the research from another researcher by adding a feature selection process, such as two algorithms from the filtered method, chi-square, and information gain, and one algorithm from the wrapped method, which is Genetic Algorithms (GA). The experiment result shows that the GA obtained result has the highest accurate value compared to the other methods. INTI International University 2023 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/1729/1/jods2023_03.pdf Muhammad, Fajar and Tri Basuki, Kurniawan and Edi Surya, Negara Harahap (2023) Analysis of Feature Selection Methods for Sentiment Analysis Concerning Covid-19 Vaccination Issues. Journal of Data Science, 2023 (03). pp. 1-13. ISSN 2805-5160 http://ipublishing.intimal.edu.my/jods.html |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software Muhammad, Fajar Tri Basuki, Kurniawan Edi Surya, Negara Harahap Analysis of Feature Selection Methods for Sentiment Analysis Concerning Covid-19 Vaccination Issues |
| title | Analysis of Feature Selection Methods for Sentiment Analysis Concerning Covid-19 Vaccination Issues |
| title_full | Analysis of Feature Selection Methods for Sentiment Analysis Concerning Covid-19 Vaccination Issues |
| title_fullStr | Analysis of Feature Selection Methods for Sentiment Analysis Concerning Covid-19 Vaccination Issues |
| title_full_unstemmed | Analysis of Feature Selection Methods for Sentiment Analysis Concerning Covid-19 Vaccination Issues |
| title_short | Analysis of Feature Selection Methods for Sentiment Analysis Concerning Covid-19 Vaccination Issues |
| title_sort | analysis of feature selection methods for sentiment analysis concerning covid-19 vaccination issues |
| topic | QA75 Electronic computers. Computer science QA76 Computer software |
| url | http://eprints.intimal.edu.my/1729/ http://eprints.intimal.edu.my/1729/ http://eprints.intimal.edu.my/1729/1/jods2023_03.pdf |