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Auto-Detection of Human Factor Contents on Social Media Posts Using Word2vec and Long Short-Term Memory (LSTM)
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Author(s): Chika Yinka-Banjo (University of Lagos, Nigeria), Gafar Lekan Raji (University of Lagos, Nigeria)and Ifeanyi Precious Ohalete (Alex-Ekwueme Federal University of Ndufu-Alike, Ikwo, Nigeria)
Copyright: 2020
Pages: 13
Source title:
Handbook of Research on the Role of Human Factors in IT Project Management
Source Author(s)/Editor(s): Sanjay Misra (Covenant University, Nigeria)and Adewole Adewumi (Covenant University, Nigeria)
DOI: 10.4018/978-1-7998-1279-1.ch001
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Abstract
The threat posed by cyberbullying to the mental health in our society cannot be overemphasized. Victims of this menace are reported to have suffered poor academic performance, depression, and suicidal thoughts. There is need to find an efficient and effective solution to this problem within the academic environment. In this research, one of the popular deep learning models—long short-term memory (LSTM)—known for its optimized performance in training sequential data was combined with Word2Vec embedding technique to create a model trained for classifying the content of social media post as containing cyberbullying content or otherwise. The result was observed to have shown improvements in its performance with respect to accuracy in the classification task with over 80% of the test dataset correctly classified as against the existing model with about 74.9% accuracy.
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