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| | * In addition, a Word Emotion Association Analysis has been also performed. This analysis complements the polarity analysis by adding more details about the kind of emotions or sentiments (joy, anger, disgust, etc.) in customer reviews. This analysis was performed by using the NRC Word-Emotion Association Lexicon. | | * In addition, a Word Emotion Association Analysis has been also performed. This analysis complements the polarity analysis by adding more details about the kind of emotions or sentiments (joy, anger, disgust, etc.) in customer reviews. This analysis was performed by using the NRC Word-Emotion Association Lexicon. |
| | <section end=Honours_in_IT-final_project /> | | <section end=Honours_in_IT-final_project /> |
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| − | 2019
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| − | '''[https://www.cct.ie/ College of Computing Technology (CCT)], Ireland'''
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| − | </p>
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| − | '''Bachelor of Science (BSc) in Information Technology'''
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| − | * Final year project: Supervised Machine Learning Models for Fake News Detection.
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| − | : To know more about this project, visit [[Supervised_Machine_Learning_for_Fake_News_Detection]]
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| − | <br />
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| − | <section begin=BSc_in_IT-final_project />
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| − | In my final Bachelor in IT project, I worked in Text classification, specifically in Supervised Machine Learning for Fake News Detection using R. In this project, we have created a Supervised Machine Learning Model for Fake News Detection based on three different algorithms: Naive Bayes, Support Vector Machine, and Gradient Boosting (XGBoost). Basically, this ML model is able to determine with an accuracy of 79% if a News Article is Fake or Reliable. Fake in the sense of News Articles that were deliberately created in order to deceive and manipulate.
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| − | <section end=BSc_in_IT-final_project />
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| | </div> | | </div> |
| | </div> | | </div> |
