Twitter sentiment analysis: An Arabic text mining approach based on COVID-19 - Albahli, Saleh (Frontiers in Public Health, 2022)
The 21st century brought a lot of innovations, among which included social media platforms advancement. These platforms brought about interactions between people and changed how news is being transmitted where people could have their say as opposed to before when only the reports were speaking. Social media has become the most influential source of speech freedom and emotions on their platforms. Anyone can express emotions using social media like Facebook, Twitter, Instagram, and YouTube. The raw data is increasing daily for every culture and field of life, so there is a need to process this raw data to get meaningful information. If any nation or country wants to know their people’s needs, there should be mined data showing the actual meaning of the people’s emotions. The COVID-19 pandemic came with many problems going beyond the virus itself, as there was mass hysteria and the spread of wrong information on social media. This problem put the whole world in turmoil and research was done to find a way to mitigate the spread of the correct news. In this research study, proposed a model of detecting genuine news related to the COVID-19 pandemic in Arabic Text using sentiment-based on data from Twitter for Gulf Countries. The proposed sentiment analysis model uses Machine Learning and SMOTE for imbalanced dataset handling. The result is the people in Gulf countries have negative sentiment during COVID-19 pandemic. This work is done so government authorities have ease of learning directly from people all across the world about the spread of COVID-19 and take appropriate actions in efforts to control it.