An efficient framework for real-time tweet classification
| Autor: | Mansaf Alam, S. N. A. Rizvi, Imran Khan, S. K. Naqvi |
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| Rok vydání: | 2017 |
| Předmět: |
Computer Networks and Communications
business.industry Computer science Applied Mathematics Big data Subject (documents) 02 engineering and technology Popularity Computer Science Applications World Wide Web Computational Theory and Mathematics Artificial Intelligence 020204 information systems 0202 electrical engineering electronic engineering information engineering Feature (machine learning) Information source 020201 artificial intelligence & image processing InformationSystems_MISCELLANEOUS Electrical and Electronic Engineering business Computer communication networks Information Systems |
| Zdroj: | International Journal of Information Technology. 9:215-221 |
| ISSN: | 2511-2112 2511-2104 |
| DOI: | 10.1007/s41870-017-0015-x |
| Popis: | Increasing popularity of social networking sites like facebook, twitter, google+ etc. is contributing in fast proliferation of big data. Amongst social Networking sites, twitter is one of the most common source of big data where people from across the world share their views on various topics and subjects. With daily Active user count of 100-million+ users twitter is becoming a rich information source for finding trends and current happenings around the world. Twitter does provide a limited “trends” feature. To make twitter trends more interesting and informative, in this paper we propose a framework that can analyze twitter data and classify tweets on some specific subject to generate trends. We illustrate the use of framework by analyzing the tweets on “Politics” domain as a subject. In order to classify tweets we propose a tweet classification algorithm that efficiently classify the tweets. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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