Auto Labeling to Increase Aspect-Based Sentiment Analysis Using K-Nearest Neighbors Method
Social media platforms generate many opinions, emotions, and views on all public services. Sentiment analysis is used in various institutions, such as universities, the business industry, and politicians. The evaluation process requires some data, both quantitative and qualitative. Researchers only...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2022-01-01
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| Series: | E3S Web of Conferences |
| Subjects: | |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2022/26/e3sconf_icenis2022_05001.pdf |
| _version_ | 1811179685747359744 |
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| author | Jazuli Ahmad Widowati Kusumaningrum Retno |
| author_facet | Jazuli Ahmad Widowati Kusumaningrum Retno |
| author_sort | Jazuli Ahmad |
| collection | DOAJ |
| description | Social media platforms generate many opinions, emotions, and views on all public services. Sentiment analysis is used in various institutions, such as universities, the business industry, and politicians. The evaluation process requires some data, both quantitative and qualitative. Researchers only focus on quantitative data but ignore qualitative data. The evaluation process given by students in the form of a review is qualitative data that is not structured, so it cannot use conventional methods. Unstructured data requires analysis as well as labeling. The labeling process of large amounts of data is a waste of time and money. Data labeling requires very high accuracy to avoid errors. Accuracy in data labeling is used for the process of classifying, training, and testing data. This study aims to automate data labeling using the K-Nearest Neighbors algorithm method. This labeling process can improve the accuracy of sentiment analysis. The results of the classification method can classify responses from Twitter users and can be used by universities as material for evaluating and assessing higher education services. The results of using a confusion matrix with 1.409 data obtained an accuracy rate of 79.43% with a value of k=15 |
| first_indexed | 2024-04-11T06:38:52Z |
| format | Article |
| id | doaj.art-0f0679822a9b4dddaaee21f4ed9d0d9c |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-04-11T06:38:52Z |
| publishDate | 2022-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-0f0679822a9b4dddaaee21f4ed9d0d9c2022-12-22T04:39:36ZengEDP SciencesE3S Web of Conferences2267-12422022-01-013590500110.1051/e3sconf/202235905001e3sconf_icenis2022_05001Auto Labeling to Increase Aspect-Based Sentiment Analysis Using K-Nearest Neighbors MethodJazuli Ahmad0Widowati1Kusumaningrum Retno2Information System, School of Postgraduate Studies, Diponegoro UniversityDepartment of Mathematics, Faculty of Sains and Mathematics, Diponegoro UniversityDepartment of Informatics, Faculty of Sains and Mathematics, Diponegoro UniversitySocial media platforms generate many opinions, emotions, and views on all public services. Sentiment analysis is used in various institutions, such as universities, the business industry, and politicians. The evaluation process requires some data, both quantitative and qualitative. Researchers only focus on quantitative data but ignore qualitative data. The evaluation process given by students in the form of a review is qualitative data that is not structured, so it cannot use conventional methods. Unstructured data requires analysis as well as labeling. The labeling process of large amounts of data is a waste of time and money. Data labeling requires very high accuracy to avoid errors. Accuracy in data labeling is used for the process of classifying, training, and testing data. This study aims to automate data labeling using the K-Nearest Neighbors algorithm method. This labeling process can improve the accuracy of sentiment analysis. The results of the classification method can classify responses from Twitter users and can be used by universities as material for evaluating and assessing higher education services. The results of using a confusion matrix with 1.409 data obtained an accuracy rate of 79.43% with a value of k=15https://www.e3s-conferences.org/articles/e3sconf/pdf/2022/26/e3sconf_icenis2022_05001.pdfauto labelingsentiment analysislearning processk-nearest neighbors |
| spellingShingle | Jazuli Ahmad Widowati Kusumaningrum Retno Auto Labeling to Increase Aspect-Based Sentiment Analysis Using K-Nearest Neighbors Method E3S Web of Conferences auto labeling sentiment analysis learning process k-nearest neighbors |
| title | Auto Labeling to Increase Aspect-Based Sentiment Analysis Using K-Nearest Neighbors Method |
| title_full | Auto Labeling to Increase Aspect-Based Sentiment Analysis Using K-Nearest Neighbors Method |
| title_fullStr | Auto Labeling to Increase Aspect-Based Sentiment Analysis Using K-Nearest Neighbors Method |
| title_full_unstemmed | Auto Labeling to Increase Aspect-Based Sentiment Analysis Using K-Nearest Neighbors Method |
| title_short | Auto Labeling to Increase Aspect-Based Sentiment Analysis Using K-Nearest Neighbors Method |
| title_sort | auto labeling to increase aspect based sentiment analysis using k nearest neighbors method |
| topic | auto labeling sentiment analysis learning process k-nearest neighbors |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2022/26/e3sconf_icenis2022_05001.pdf |
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