Document Details
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Document Type |
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Thesis |
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Document Title |
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USING TWITTER TO ANALYZE INITIATIVES OF A STARTUP COMPANY: THE CASE OF EMPOWERING WOMEN IN CAREEM. استخدام تويتر لتحليل مبادرات الشركات الناشئة: دراسة حالة تمكين المراءة للعمل في شركة كريم. |
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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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Public opinions are significant to almost any organization, companies, and governments, in which such entities are aware of the significance of utilizing the unstructured data in the social networks, and it has become a growing research area lately. Twitter, as a microblogging platform, represents a significant source of public opinions that is easily accessible. In the business domain, the previous research efforts in analyzing startups activities through Twitter analysis are generally limited, especially for the Arabic language. In the Twitter analysis filed, there is a lack of a twitter-based analytics framework that combines different analysis methods to utilize the Twitter dataset better.
This thesis study aims to fill the literature research gaps by proposing a Twitter analytics-based framework called Startup Initiatives Response Analysis (SIRA). SIRA assesses the performance of an initiative taken by startup using text classification, sentiment analysis, and statistical analysis techniques. The proposed framework is validated empirically through a case study of Arabic startup (i.e., Careem), regarding the initiative of empowering women to work in the Careem. The study experiment was carried out based on using supervised machine learning (SML) in building the subject and sentiment classification models. As well, the classification models are evaluated through a comparative analysis in terms of examining a variety of machine learning (ML) classifiers, and various levels of preprocessing techniques to improve the performance of Arabic text mining.
The study experiment yielded the following results: for both two Arabic classification models, Complement Naïve Bayes (CNB) achieved a higher F1 measure with applying text cleaning and normalization as text preprocessing techniques. While the Neural Network (NN) classifier achieved the highest F1 measure for the binary classification model of the English dataset, and the Random Forest (RF) classifier outperformed other classifiers for the sentiment classification model of the English dataset.
In contrast, based on the classified dataset, several statistical analyses were conducted and presented (e.g., Tweets Reply frequency, the temporal distribution of Tweets). The experiment results analysis confirms the effectiveness of such a framework in delivering valuable insights regarding the public responsiveness, based on a comprehensive qualitative and quantitative analysis of the Twitter dataset.
The proposed framework (SIRA), is applicable as a Twitter-based analytics framework in any domain and for any purpose. One of the future work recommendations is validating the proposed framework with other experiments of different datasets. |
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Supervisor |
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Dr. Muhammad Ahtisham Aslam |
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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, March 11, 2020 |
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Researchers
| بشاير علي العتيبي | Al otaibi, Bashayer Ali | Researcher | Master | |
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