COMPARISON OF DIFFERENT CLASSIFICATION MODELS FOR SENTIMENT ANALYSIS
| dc.contributor.author | Makhul M. | |
| dc.date.accessioned | 2024-02-01T06:11:04Z | |
| dc.date.available | 2024-02-01T06:11:04Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | Abstract. In this work, we explored sentiment analysis techniques of texts using the example of product comments in the Kazakh language. To do this, we used machine learning methods such as Naive Bayes, Random Forest, Logistic Regression and Support Vector Machine, as well as text processing tools: CountVectorizer and TfidfVectorizer. In the process of work, experiments were carried out with different configurations of models and parameters of vectorizers. To assess the quality of the models, we used accuracy, precision, recall and F1-score metrics. The research findings indicated that the application of machine learning techniques make it possible to achieve high accuracy in sentiment analysis of comments. The best results were obtained using the Support Vector Machine and TfidfVectorizer. This study can be used to further improve the systems for sentiment analysis of comments in the Kazakh language, which can be useful in monitoring public opinion in various areas, including business. | |
| dc.identifier.citation | Makhul Maulen / COMPARISON OF DIFFERENT CLASSIFICATION MODELS FOR SENTIMENT ANALYSIS / СДУ хабаршысы - 2023 | |
| dc.identifier.issn | 2709-2631 | |
| dc.identifier.uri | https://repository.sdu.edu.kz/handle/123456789/1170 | |
| dc.language.iso | en | |
| dc.publisher | СДУ хабаршысы - 2023 | |
| dc.subject | Kazakh language | |
| dc.subject | sentiment analysis | |
| dc.subject | Naive Bayes | |
| dc.subject | Random Forest | |
| dc.subject | Support Vector Machine | |
| dc.subject | Logistic Regression | |
| dc.subject | Scikit-learn | |
| dc.subject | СДУ хабаршысы - 2023 | |
| dc.subject | №3 | |
| dc.title | COMPARISON OF DIFFERENT CLASSIFICATION MODELS FOR SENTIMENT ANALYSIS | |
| dc.type | Article |