Twitter Social Media Conversion Topic Trending Analysis Using Latent Dirichlet Allocation Algorithm
In Indonesia, Twitter is one of the most widely used social media platforms. Because of the diverse and frequently shifting message patterns on this social media, it is extremely challenging and time-consuming to manually identify topics from a collection of messages. Topic modeling is one method f...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Yayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI)
2022-12-01
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| Series: | Journal of Applied Engineering and Technological Science |
| Subjects: | |
| Online Access: | http://www.yrpipku.com/journal/index.php/jaets/article/view/1143 |
| _version_ | 1827860432843440128 |
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| author | Musliadi K H Hazriani Zainuddin Yuyun Wabula |
| author_facet | Musliadi K H Hazriani Zainuddin Yuyun Wabula |
| author_sort | Musliadi K H |
| collection | DOAJ |
| description |
In Indonesia, Twitter is one of the most widely used social media platforms. Because of the diverse and frequently shifting message patterns on this social media, it is extremely challenging and time-consuming to manually identify topics from a collection of messages. Topic modeling is one method for obtaining information from social media. The model and visualization of the results of modeling topics that are discussed on social media by the Makassar community are the goals of this study. The Latent Dirichlet Allocation (LDA) algorithm is used to model and display the results of this study. The modeling results indicate that the eighth topic is the most frequently used word in a conversation. In the meantime, the 7th and 6th topics emerged as the conversation's core based on the spread of the words with the highest term frequency. The study's findings led the researchers to the conclusion that in the Makassar community's social media discussions, capitalization and visualization using the LDA method produced the words with the highest trend and the topic with the highest term frequency.
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| first_indexed | 2024-03-12T13:23:56Z |
| format | Article |
| id | doaj.art-0f1ce91bb4524d1ebc71e2ad9de0feab |
| institution | Directory Open Access Journal |
| issn | 2715-6087 2715-6079 |
| language | English |
| last_indexed | 2024-03-12T13:23:56Z |
| publishDate | 2022-12-01 |
| publisher | Yayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI) |
| record_format | Article |
| series | Journal of Applied Engineering and Technological Science |
| spelling | doaj.art-0f1ce91bb4524d1ebc71e2ad9de0feab2023-08-25T11:29:34ZengYayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI)Journal of Applied Engineering and Technological Science2715-60872715-60792022-12-014110.37385/jaets.v4i1.1143Twitter Social Media Conversion Topic Trending Analysis Using Latent Dirichlet Allocation AlgorithmMusliadi K H0Hazriani Zainuddin1Yuyun Wabula2Universitas UniversalSTMIK Handayani MakassarSTMIK Handayani Makassar In Indonesia, Twitter is one of the most widely used social media platforms. Because of the diverse and frequently shifting message patterns on this social media, it is extremely challenging and time-consuming to manually identify topics from a collection of messages. Topic modeling is one method for obtaining information from social media. The model and visualization of the results of modeling topics that are discussed on social media by the Makassar community are the goals of this study. The Latent Dirichlet Allocation (LDA) algorithm is used to model and display the results of this study. The modeling results indicate that the eighth topic is the most frequently used word in a conversation. In the meantime, the 7th and 6th topics emerged as the conversation's core based on the spread of the words with the highest term frequency. The study's findings led the researchers to the conclusion that in the Makassar community's social media discussions, capitalization and visualization using the LDA method produced the words with the highest trend and the topic with the highest term frequency. http://www.yrpipku.com/journal/index.php/jaets/article/view/1143Topic AnalysisLDATrending Twitter TopicsTwitter Conversation Topics |
| spellingShingle | Musliadi K H Hazriani Zainuddin Yuyun Wabula Twitter Social Media Conversion Topic Trending Analysis Using Latent Dirichlet Allocation Algorithm Journal of Applied Engineering and Technological Science Topic Analysis LDA Trending Twitter Topics Twitter Conversation Topics |
| title | Twitter Social Media Conversion Topic Trending Analysis Using Latent Dirichlet Allocation Algorithm |
| title_full | Twitter Social Media Conversion Topic Trending Analysis Using Latent Dirichlet Allocation Algorithm |
| title_fullStr | Twitter Social Media Conversion Topic Trending Analysis Using Latent Dirichlet Allocation Algorithm |
| title_full_unstemmed | Twitter Social Media Conversion Topic Trending Analysis Using Latent Dirichlet Allocation Algorithm |
| title_short | Twitter Social Media Conversion Topic Trending Analysis Using Latent Dirichlet Allocation Algorithm |
| title_sort | twitter social media conversion topic trending analysis using latent dirichlet allocation algorithm |
| topic | Topic Analysis LDA Trending Twitter Topics Twitter Conversation Topics |
| url | http://www.yrpipku.com/journal/index.php/jaets/article/view/1143 |
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