A Social Media Sentiment Analysis Using Machine Learning Approaches
Social media platforms like Twitter provide major means for individuals to express their opinions on various topics; therefore, a need for complex tools to distinguish between negative and positive attitudes in textual content. With consideration for the most suitable models for precisely classifyi...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Tikrit University
2025-08-01
|
| Series: | Tikrit Journal of Pure Science |
| Subjects: | |
| Online Access: | https://www.tjpsj.org/index.php/tjps/article/view/1916 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849222891973902336 |
|---|---|
| author | Noor Salah Irzooqi Al-Agele Didem KIVANÇ TÜRELİ |
| author_facet | Noor Salah Irzooqi Al-Agele Didem KIVANÇ TÜRELİ |
| author_sort | Noor Salah Irzooqi Al-Agele |
| collection | DOAJ |
| description |
Social media platforms like Twitter provide major means for individuals to express their opinions on various topics; therefore, a need for complex tools to distinguish between negative and positive attitudes in textual content. With consideration for the most suitable models for precisely classifying sentiments within social media data, this study aims to evaluate the efficacy of machine learning algorithms in analyzing sentiment text that people post or comment on Twitter, thus bridging the research gap in the analysis of sentiment in an understudied domain. a set of machine learning algorithms was applied along with feature extraction methods, including TF-IDF and Natural Language Processing (NLP). With an accuracy of 93%, the Random Forest (RF) model proved to be the most effective among other models, Because of its exceptional capacity and generating accurate and dependable results on textual data, the Random Forest (RF) model proves in the study to be the most optimal choice for sentiment analysis textual.
|
| format | Article |
| id | doaj-art-cc9b38fe67e8446d9e42d9949d679ad3 |
| institution | Kabale University |
| issn | 1813-1662 2415-1726 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Tikrit University |
| record_format | Article |
| series | Tikrit Journal of Pure Science |
| spelling | doaj-art-cc9b38fe67e8446d9e42d9949d679ad32025-08-26T02:38:17ZengTikrit UniversityTikrit Journal of Pure Science1813-16622415-17262025-08-0130410.25130/tjps.v30i4.1916A Social Media Sentiment Analysis Using Machine Learning ApproachesNoor Salah Irzooqi Al-Agele0https://orcid.org/0009-0001-8568-1989Didem KIVANÇ TÜRELİ1Department of Advanced Electronics and Communication Technology, University of Istanbul Okan-TurkeyDepartment of Electrical and Electronics Engineering, University of Washington-United States of America. Social media platforms like Twitter provide major means for individuals to express their opinions on various topics; therefore, a need for complex tools to distinguish between negative and positive attitudes in textual content. With consideration for the most suitable models for precisely classifying sentiments within social media data, this study aims to evaluate the efficacy of machine learning algorithms in analyzing sentiment text that people post or comment on Twitter, thus bridging the research gap in the analysis of sentiment in an understudied domain. a set of machine learning algorithms was applied along with feature extraction methods, including TF-IDF and Natural Language Processing (NLP). With an accuracy of 93%, the Random Forest (RF) model proved to be the most effective among other models, Because of its exceptional capacity and generating accurate and dependable results on textual data, the Random Forest (RF) model proves in the study to be the most optimal choice for sentiment analysis textual. https://www.tjpsj.org/index.php/tjps/article/view/1916Sentiment Analysis, Machine Learning (ML), Random Forest (RF), Natural Language Processing (NLP), Feature Extraction, Fake news. |
| spellingShingle | Noor Salah Irzooqi Al-Agele Didem KIVANÇ TÜRELİ A Social Media Sentiment Analysis Using Machine Learning Approaches Tikrit Journal of Pure Science Sentiment Analysis, Machine Learning (ML), Random Forest (RF), Natural Language Processing (NLP), Feature Extraction, Fake news. |
| title | A Social Media Sentiment Analysis Using Machine Learning Approaches |
| title_full | A Social Media Sentiment Analysis Using Machine Learning Approaches |
| title_fullStr | A Social Media Sentiment Analysis Using Machine Learning Approaches |
| title_full_unstemmed | A Social Media Sentiment Analysis Using Machine Learning Approaches |
| title_short | A Social Media Sentiment Analysis Using Machine Learning Approaches |
| title_sort | social media sentiment analysis using machine learning approaches |
| topic | Sentiment Analysis, Machine Learning (ML), Random Forest (RF), Natural Language Processing (NLP), Feature Extraction, Fake news. |
| url | https://www.tjpsj.org/index.php/tjps/article/view/1916 |
| work_keys_str_mv | AT noorsalahirzooqialagele asocialmediasentimentanalysisusingmachinelearningapproaches AT didemkivanctureli asocialmediasentimentanalysisusingmachinelearningapproaches AT noorsalahirzooqialagele socialmediasentimentanalysisusingmachinelearningapproaches AT didemkivanctureli socialmediasentimentanalysisusingmachinelearningapproaches |