Twitter sentiment analysis: An Arabic text mining approach based on COVID-19.

The 21st century has seen a lot of innovations, among which included the advancement of social media platforms. These platforms brought about interactions between people and changed how news is transmitted, with people now able to voice their opinion as opposed to before where only the reporters 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 platforms 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 into turmoil and research was done to find a way to mitigate the spread of incorrect news. In this research study, we have proposed a model of detecting genuine news related to the COVID-19 pandemic in Arabic Text using sentiment-based data from Twitter for Gulf countries. The proposed sentiment analysis model uses Machine Learning and SMOTE for imbalanced dataset handling. The result showed the people in Gulf countries had a negative sentiment during COVID-19 pandemic. This work was done so government authorities can easily learn directly from people all across the world about the spread of COVID-19 and take appropriate actions in efforts to control it.

Detecting Presence of PTSD Using Sentiment Analysis From Text Data.

Rates of Post-traumatic stress disorder (PTSD) have risen significantly due to the COVID-19 pandemic. Telehealth has emerged as a means to monitor symptoms for such disorders. This is partly due to isolation or inaccessibility of therapeutic intervention caused from the pandemic. Additional screening tools may be needed to augment identification and diagnosis of PTSD through a virtual medium. Sentiment analysis refers to the use of natural language processing (NLP) to extract emotional content from text information. In our study, we train a machine learning (ML) model on text data, which is part of the Audio/Visual Emotion Challenge and Workshop (AVEC-19) corpus, to identify individuals with PTSD using sentiment analysis from semi-structured interviews. Our sample size included 188 individuals without PTSD, and 87 with PTSD. The interview was conducted by an artificial character (Ellie) over a video-conference call. Our model was able to achieve a balanced accuracy of 80.4% on a held out dataset used from the AVEC-19 challenge. Additionally, we implemented various partitioning techniques to determine if our model was generalizable enough. This shows that learned models can use sentiment analysis of speech to identify the presence of PTSD, even through a virtual medium. This can serve as an important, accessible and inexpensive tool to detect mental health abnormalities during the COVID-19 pandemic.