Setiabudi, Reza and Iswari, Ni Made Satvika and Rusli, Andre (2021) Enhancing text classification performance by preprocessing misspelled words in Indonesian language. TELKOMNIKA Telecommunication, Computing, Electronics and Control, 19 (4). ISSN 1693-6930
Full text not available from this repository.Abstract
Supervised learning using shallow machine learning methods is still a popular method in processing text, despite the rapidly advancing sector of unsupervised methodologies using deep learning. Supervised text classification for application user feedback sentiments in Indonesian Language is one of the applications which is quite popular in both the research community and industry. However, due to the nature of shallow machine learning approaches, various text preprocessing techniques are required to clean the input data. This research aims to implement and evaluate the role of Levenshtein distance algorithm in detecting and preprocessing misspelled words in Indonesian language, before the text data is then used to train a user feedback sentiment classification model using multinomial Naïve Bayes. This research experimented with various evaluation scenarios, and found that preprocessing misspelled words in Indonesian language using the Levenshtein distance algorithm could be useful and showed a promising 8.2% increase on the accuracy of the model’s ability to classify user feedback text according to their sentiments.
| Item Type: | Article |
|---|---|
| Subjects: | 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 005 Computer Programming |
| Divisions: | Faculty of Engineering & Informatics > Informatics |
| Depositing User: | Administrator UMN Library |
| Date Deposited: | 13 Oct 2021 07:52 |
| Last Modified: | 13 Oct 2021 07:52 |
| URI: | https://kc.umn.ac.id/id/eprint/18729 |
Actions (login required)
 |
View Item |
