Ascertaining polarity of public opinions on Bangladesh cricket using machine learning techniques
| Autor: | Partha Chakraborty, Thipendra Pal Singh, Saifur Rahman, M. Abdullah Faruque, Tanupriya Choudhury, Jung-Sup Um |
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| Rok vydání: | 2021 |
| Předmět: |
010504 meteorology & atmospheric sciences
Computer science Polarity (physics) Geography Planning and Development 0211 other engineering and technologies 02 engineering and technology Machine learning computer.software_genre 01 natural sciences Naive Bayes classifier Artificial Intelligence Feature (machine learning) Social media Computers in Earth Sciences 021101 geological & geomatics engineering 0105 earth and related environmental sciences Point (typography) business.industry Sentiment analysis language.human_language Computer Science Applications Support vector machine Bengali language Artificial intelligence business computer |
| Zdroj: | Spatial Information Research. 30:1-8 |
| ISSN: | 2366-3294 2366-3286 |
| DOI: | 10.1007/s41324-021-00403-8 |
| Popis: | In the present world, we are not only the consumers of information but creators as well. The virtual world of social media, which is considered a free open forum for discussion; provides its participants a chance to shape or re-shape the digital information by expressing opinions. These opinions generally contain different types of sentiments. Sentiment analysis is a tool that performs the computational study of identifying and extracting sentiment content of textual data that can be used to classify those public opinions posted on various topics in social media. In this paper, a sentiment polarity detection approach is presented, that detects the polarity of textual Facebook posts in Bangla containing people's point of views on Bangladesh Cricket using three popular supervised machine learning algorithms named Naive Bayes (NB), support vector machines (SVM), and logistic regression (LR). Comparative result analysis is also provided between classifiers, where LR performed slightly better than SVM and NB by considering n-gram as a feature with an accuracy of 83%. |
| Databáze: | OpenAIRE |
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