Edi, Surya Negara (2022) Topic Modelling Twitter Data with Latent Dirichlet Allocation Method. Topic Modelling Twitter Data with Latent Dirichlet Allocation Method.
Full text not available from this repository.Abstract
Twitter is a popular social media for every user to issue thoughts and emotional forms which are tweets, tweets that only have 140 characters with limitations to write in text. Twitter is one of the social media places to get information that is always up to date, tweets are categorized into big data because tweets are information that can be used as a source of data for research. Latent Dirichlet Allocation (LDA) as an algorithm that can process large text data (big data). In this study using the LDA method as an algorithm to produce topic modeling, each topic similarity, and visualization of topic clusters from the tweet data generated as many as 4 topics (Economic, Military, Sports, Technology) in Indonesian, where each topic has a number different tweets. The LDA method used in the processing of tweet data is successfully carried out and works optimally, in each topic extraction, topic modeling, generating index words that are in each topic cluster and computer visualization in the topic.LDA output shows optimal performance in the process of word indexing in Sport topics with 1260 tweets with an accuracy of 98% better than the LSI method in Topic Modeling.
| Item Type: | Article |
|---|---|
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering, Science and Mathematics > School of Civil Engineering and the Environment |
| Depositing User: | Mr Edi Surya Negara |
| Date Deposited: | 24 Jun 2022 15:20 |
| Last Modified: | 24 Jun 2022 15:20 |
| URI: | http://eprints.binadarma.ac.id/id/eprint/15490 |
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