Sentiment Analysis for Detecting Cyberbullying Using TF-IDF and SVM
Social media has become a new method of today’s communication in a new digitalize era. Children and adults have used social media a lot in interacting with others. Therefore social media has shifted conventional communication into digital one. This digital development on social media is a serious pr...
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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2020-12-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| Subjects: | |
| Online Access: | http://jurnal.iaii.or.id/index.php/RESTI/article/view/2753 |
| _version_ | 1797334009465798656 |
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| author | Wahyu Adi Prabowo Fitriani Azizah |
| author_facet | Wahyu Adi Prabowo Fitriani Azizah |
| author_sort | Wahyu Adi Prabowo |
| collection | DOAJ |
| description | Social media has become a new method of today’s communication in a new digitalize era. Children and adults have used social media a lot in interacting with others. Therefore social media has shifted conventional communication into digital one. This digital development on social media is a serious problem that must be faced because it has been found that there are more and more acts of cyberbullying. This act of cyberbullying can attack the psychic, causing depression up to suicide. The dangers of cyberbullying are troubling and cause concern to the community. Therefore, this study will analyze the sentiment on the comments contained on social media to find out the value of sentiment from comments on social media platforms. The comment data will be processed at the preprocessing stage, Term Frequency-Inverse Document Frequency (TF-IDF), and the Support Vector Machine (SVM) classification method. Comment data to be classified as 1500 data taken using crawling data through libraries in python programming and divided into 80% data training and 20% data testing. Based on the results of the test, the accuracy value is 93%, the precision value is 95%, and the recall value is 97%. In this research, a system model design is also carried out where the system can be integrated with the browser to open a user page on the classification of comments that have been input into the system. |
| first_indexed | 2024-03-08T08:13:35Z |
| format | Article |
| id | doaj.art-84a2cd74f56d41f190abb61067cdc2e3 |
| institution | Directory Open Access Journal |
| issn | 2580-0760 |
| language | English |
| last_indexed | 2024-03-08T08:13:35Z |
| publishDate | 2020-12-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj.art-84a2cd74f56d41f190abb61067cdc2e32024-02-02T08:00:28ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602020-12-01461142 – 11481142 – 114810.29207/resti.v4i6.27532753Sentiment Analysis for Detecting Cyberbullying Using TF-IDF and SVMWahyu Adi Prabowo0Fitriani Azizah1Institut Teknologi Telkom PurwokertoInstitut Teknologi Telkom PurwokertoSocial media has become a new method of today’s communication in a new digitalize era. Children and adults have used social media a lot in interacting with others. Therefore social media has shifted conventional communication into digital one. This digital development on social media is a serious problem that must be faced because it has been found that there are more and more acts of cyberbullying. This act of cyberbullying can attack the psychic, causing depression up to suicide. The dangers of cyberbullying are troubling and cause concern to the community. Therefore, this study will analyze the sentiment on the comments contained on social media to find out the value of sentiment from comments on social media platforms. The comment data will be processed at the preprocessing stage, Term Frequency-Inverse Document Frequency (TF-IDF), and the Support Vector Machine (SVM) classification method. Comment data to be classified as 1500 data taken using crawling data through libraries in python programming and divided into 80% data training and 20% data testing. Based on the results of the test, the accuracy value is 93%, the precision value is 95%, and the recall value is 97%. In this research, a system model design is also carried out where the system can be integrated with the browser to open a user page on the classification of comments that have been input into the system.http://jurnal.iaii.or.id/index.php/RESTI/article/view/2753preprocessing, term frequency and inverse document frequency, support vector machine, confusion matrix, application, sentiment analysis |
| spellingShingle | Wahyu Adi Prabowo Fitriani Azizah Sentiment Analysis for Detecting Cyberbullying Using TF-IDF and SVM Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) preprocessing, term frequency and inverse document frequency, support vector machine, confusion matrix, application, sentiment analysis |
| title | Sentiment Analysis for Detecting Cyberbullying Using TF-IDF and SVM |
| title_full | Sentiment Analysis for Detecting Cyberbullying Using TF-IDF and SVM |
| title_fullStr | Sentiment Analysis for Detecting Cyberbullying Using TF-IDF and SVM |
| title_full_unstemmed | Sentiment Analysis for Detecting Cyberbullying Using TF-IDF and SVM |
| title_short | Sentiment Analysis for Detecting Cyberbullying Using TF-IDF and SVM |
| title_sort | sentiment analysis for detecting cyberbullying using tf idf and svm |
| topic | preprocessing, term frequency and inverse document frequency, support vector machine, confusion matrix, application, sentiment analysis |
| url | http://jurnal.iaii.or.id/index.php/RESTI/article/view/2753 |
| work_keys_str_mv | AT wahyuadiprabowo sentimentanalysisfordetectingcyberbullyingusingtfidfandsvm AT fitrianiazizah sentimentanalysisfordetectingcyberbullyingusingtfidfandsvm |