Sentiment Analysis on Social Media with Glove Using Combination CNN and RoBERTa
Twitter is a popular social media platform that allows users to share short message’s opinion and engage in real-time conversations on a wide range of topics known as tweet. However, tweets often have a complicated and unclear context, which makes it difficult to determine the actual emotion. Theref...
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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2023-06-01
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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/4892 |
| _version_ | 1797328025448087552 |
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| author | Diaz Tiyasya Putra Erwin Budi Setiawan |
| author_facet | Diaz Tiyasya Putra Erwin Budi Setiawan |
| author_sort | Diaz Tiyasya Putra |
| collection | DOAJ |
| description | Twitter is a popular social media platform that allows users to share short message’s opinion and engage in real-time conversations on a wide range of topics known as tweet. However, tweets often have a complicated and unclear context, which makes it difficult to determine the actual emotion. Therefore, sentiment analysis is required to see the tendency of an opinion, whether the opinion tends to be positive, negative, or neutral. Researchers or institutions can find out how the response and emotions of an issue are happening and make good decisions. With the large user of Twitter social media in Indonesia, sentiment analysis will be carried out using deep learning Convolutional Neural Network (CNN), Term Frequency-Inverse Document Frequency (TF-IDF), Robustly Optimized BERT Pretraining Approach (RoBERTa), Synthetic Minority Over-sampling Technique (SMOTE), and Global Vector (Glove). In this research, the dataset used is trending topics with hashtags related to government policies on Twitter social media and obtained through crawling. By using 30.811 data, the result shows the highest accuracy of 95.56% using CNN with a split ratio of 90:10, baseline unigram, RoBERTa, SMOTE, and Top10 corpus tweet with an increase 10.1%. |
| first_indexed | 2024-03-08T06:45:58Z |
| format | Article |
| id | doaj.art-28285901548443e2bbf497e2f0ee6bad |
| institution | Directory Open Access Journal |
| issn | 2580-0760 |
| language | English |
| last_indexed | 2024-03-08T06:45:58Z |
| publishDate | 2023-06-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj.art-28285901548443e2bbf497e2f0ee6bad2024-02-03T07:57:25ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602023-06-017345756310.29207/resti.v7i3.48924892Sentiment Analysis on Social Media with Glove Using Combination CNN and RoBERTaDiaz Tiyasya Putra0Erwin Budi Setiawan1Telkom UniversityTelkom UniversityTwitter is a popular social media platform that allows users to share short message’s opinion and engage in real-time conversations on a wide range of topics known as tweet. However, tweets often have a complicated and unclear context, which makes it difficult to determine the actual emotion. Therefore, sentiment analysis is required to see the tendency of an opinion, whether the opinion tends to be positive, negative, or neutral. Researchers or institutions can find out how the response and emotions of an issue are happening and make good decisions. With the large user of Twitter social media in Indonesia, sentiment analysis will be carried out using deep learning Convolutional Neural Network (CNN), Term Frequency-Inverse Document Frequency (TF-IDF), Robustly Optimized BERT Pretraining Approach (RoBERTa), Synthetic Minority Over-sampling Technique (SMOTE), and Global Vector (Glove). In this research, the dataset used is trending topics with hashtags related to government policies on Twitter social media and obtained through crawling. By using 30.811 data, the result shows the highest accuracy of 95.56% using CNN with a split ratio of 90:10, baseline unigram, RoBERTa, SMOTE, and Top10 corpus tweet with an increase 10.1%.http://jurnal.iaii.or.id/index.php/RESTI/article/view/4892sentiment analysiscnntwitterrobertaglove |
| spellingShingle | Diaz Tiyasya Putra Erwin Budi Setiawan Sentiment Analysis on Social Media with Glove Using Combination CNN and RoBERTa Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) sentiment analysis cnn roberta glove |
| title | Sentiment Analysis on Social Media with Glove Using Combination CNN and RoBERTa |
| title_full | Sentiment Analysis on Social Media with Glove Using Combination CNN and RoBERTa |
| title_fullStr | Sentiment Analysis on Social Media with Glove Using Combination CNN and RoBERTa |
| title_full_unstemmed | Sentiment Analysis on Social Media with Glove Using Combination CNN and RoBERTa |
| title_short | Sentiment Analysis on Social Media with Glove Using Combination CNN and RoBERTa |
| title_sort | sentiment analysis on social media with glove using combination cnn and roberta |
| topic | sentiment analysis cnn roberta glove |
| url | http://jurnal.iaii.or.id/index.php/RESTI/article/view/4892 |
| work_keys_str_mv | AT diaztiyasyaputra sentimentanalysisonsocialmediawithgloveusingcombinationcnnandroberta AT erwinbudisetiawan sentimentanalysisonsocialmediawithgloveusingcombinationcnnandroberta |