Non-linear Kernel Optimisation of Support Vector Machine Algorithm for Online Marketplace Sentiment Analysis
| Autor: | Abdul Fadlil, Imam Riadi, Fiki Andrianto |
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| Jazyk: | indonéština |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Jurnal Informatika, Vol 12, Iss 1, Pp 29-38 (2024) |
| Druh dokumentu: | article |
| ISSN: | 2086-9398 2579-8901 |
| DOI: | 10.30595/juita.v12i1.19798 |
| Popis: | Twitter is a social media platform that is very important in the digital world. Fast communication and interaction make Twitter a vital information center in sentiment analysis. The purpose of this research is to classify public opinion about the presence of marketplaces in Indonesia, both positive and negative sentiments, using a Non-linear SVM algorithm based on 1276 tweets. This research involves the stages of data pre-processing, labeling, feature extraction using TF-IDF, and data division into three scenarios: 80% training data and 20% test data, 50% training data and 50% test data scenario, and 20% training data and 80% test data scenario. The last process, GridSearchCV, combines cross-validation and non-linear SVM parameters for model evaluation using a confusion matrix. The best SVM model resulting from the scenario was 80% training and 20% test data, with hyperparameters Gamma = 100 and C = 0.01, achieving 89% accuracy. When tested on never-before-seen data, the accuracy increased to 90%, with an f1-score of 91%, precision of 88%, and recall of 95% on negative sentiments. In conclusion, evaluating the performance of non-linear SVM kernels with a combination of hyperparameter values can improve accuracy, especially on public response information about online marketplaces and public sentiment. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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