A comparative study of sentiment analysis using SVM and SentiWordNet
| Dublin Core | PKP Metadata Items | Metadata for this Document | |
| 1. | Title | Title of document | A comparative study of sentiment analysis using SVM and SentiWordNet |
| 2. | Creator | Author's name, affiliation, country | Mohammad Fikri; Institut Teknologi Sepuluh Nopember; Indonesia |
| 2. | Creator | Author's name, affiliation, country | Riyanarto Sarno; Institut Teknologi Sepuluh Nopember; Indonesia |
| 3. | Subject | Discipline(s) | Machine Learning; Natural Language Processing; Text Mining |
| 3. | Subject | Keyword(s) | Sentiment analysis; Sentiwordnet; Wordnet; Rule-based; Support Vector Machine |
| 4. | Description | Abstract | Sentiment analysis has grown rapidly which impact on the number of services using the internet popping up in Indonesia. In this research, the sentiment analysis uses the rule-based method with the help of SentiWordNet and Support Vector Machine (SVM) algorithm with Term Frequency–Inverse Document Frequency (TF-IDF) as feature extraction method. Since the number of sentences in positive, negative and neutral classes is imbalanced, the oversampling method is implemented. For imbalanced dataset, the rule-based SentiWordNet and SVM algorithm achieve accuracies of 56% and 76%, respectively. However, for the balanced dataset, the rule-based SentiWordNet and SVM algorithm achieve accuracies of 52% and 89%, respectively. |
| 5. | Publisher | Organizing agency, location | Institute of Advanced Engineering and Science |
| 6. | Contributor | Sponsor(s) | |
| 7. | Date | (YYYY-MM-DD) | 2019-03-01 |
| 8. | Type | Status & genre | Peer-reviewed Article |
| 8. | Type | Type | |
| 9. | Format | File format | |
| 10. | Identifier | Uniform Resource Identifier | https://ijeecs.iaescore.com/index.php/IJEECS/article/view/13333 |
| 10. | Identifier | Digital Object Identifier (DOI) | http://doi.org/10.11591/ijeecs.v13.i3.pp902-909 |
| 11. | Source | Title; vol., no. (year) | Indonesian Journal of Electrical Engineering and Computer Science; Vol 13, No 3: March 2019 |
| 12. | Language | English=en | en |
| 14. | Coverage | Geo-spatial location, chronological period, research sample (gender, age, etc.) | |
| 15. | Rights | Copyright and permissions |
Copyright (c) 2018 Institute of Advanced Engineering and Science This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |