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Document Type |
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Thesis |
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Document Title |
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A MACHINE LEARNING BASED FRAMEWORK FOR ANALYZING TOPICS DISCUSSED BY SAUDI UNIVERSITIES ON TWITTER إطار نظري لفهم وتحليل واكتشاف المواضيع الأكثر تداولاً في حسابات الجامعات السعودية في تويتر |
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Subject |
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Faculty of Computing and Information Technology |
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Document Language |
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Arabic |
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Abstract |
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Twitter, a microblogging service, emerged as a tool for communication. It is not only restricted to users’ social life, also it serves as a medium of sharing information and exchanging emerging ideas all over the globe especially in Saudi Arabia. In fact, many Saudi Universities have started to use Twitter as a new broadcast tool for many purposes such as disseminating and discussing different topics related to their activities. However, there has been a little research to explore these different topics that are disseminated by Saudi Universities and the associated sentiments of the crowd with these different shared topics. This research aims to explore major topics and their associated sentiments. Firstly, we explored tweet sentiment analysis techniques to find the tweet sentiments regarding Saudi universities using machine learning models. Furthermore, in order to understand the topics being discussed by Saudi universities, we employ Latent Dirichlet Allocation (LDA) topic modeling approach to find the main topics on a dataset of 2,49,219 tweets. Using LDA, we find five major positive topics and six major negative topics discussed in Arabic tweets of Saudi universities. Overall, this study contributes in getting better insights about the usage of social media by Saudi universities in identifying the topics (e.g., admissions, complaints, news, announcements, etc.) discussed by Saudi universities with the associated sentiment. The possible implications of this study are to provide research informed evidence to decision makers on how public sentiments can be used for the effective policy making. |
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Supervisor |
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Dr. Mohammed Basheri |
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Thesis Type |
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Master Thesis |
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Publishing Year |
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1441 AH
2020 AD |
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Added Date |
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Wednesday, June 24, 2020 |
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Researchers
| إيمان راضي الجهني | Aljohani, Eman Radi | Researcher | Master | |
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