![]() Furthermore, this problem will force decision makers to act quickly to prevent the spread of an event or even quarantine confirmed instances of infectious diseases. In particularly dangerous circumstances, such as disease outbreaks, decision makers face obstacles such as detecting people’s attitudes on a subject and their views towards public health policy decision-making. Two parties were primarily targeted when scanning the issues related to sentiment analysis and infectious diseases and outbreaks, in addition to viruses, epidemics, and pandemics: (1) decision makers and (2) the scientific community. Issues relating to the community individuals or parties from other fields or agencies are welcome to join the community in this section. Other researchers talked about difficulties of social media platforms due to the cadence of content capability limitations, possible exaggeration, and challenges in comprehending emotions, particularly in situations such as epidemics or different sources. Researchers in the field of sentiment analysis highlighted concerns and obstacles of the social media platform in terms of trustworthiness and authenticity. Even these priceless assets face several obstacles that could stymie their final implementation. Facebook, Twitter, Reddit, Instagram, and news forums are some of the most well-known social media sites that are heavily used in sentiment analysis. This method can provide a low-cost, quick, and effective public health monitoring system on a large scale. Because created data are very dynamic and relevant for real-time trends, social media analysis is a promising field. Many academics from all over the world have looked into the use of social media in a variety of sectors, with one of the most important being their involvement during disease outbreaks. Users can quickly convey their sentiments on social media. Social media, as a rapidly increasing online forum for exchanging opinions and ideas, offers many chances for decision makers to better comprehend public sentiment. Social media platforms provide several challenges. Sentiment analysis is the “process of analyzing text with the help of machine learning and natural language processing (NLP) methods to identify the polarity of text” for a better emotional understanding of individuals or social groups. Various social media channels have a significant impact on boosting public knowledge about the disease’s importance and advocating preventive measures among community members. Twitter and other social media platforms are considered huge data repositories that may be used to collect data for studying human psychology and behavior so as to get a deeper understanding of psychology and health. This sickness has had profound effects on people in both explicit and tacit ways. The COVID-19 epidemic was claiming the lives of people across the planet. The globe was in terrible condition as a result of the sudden spread of the coronavirus. Keeping a track of mental health of individuals throughout a variety of events and topics will be essential for making appropriate decisions. Governments have long employed lockdowns and social isolation against mental health of the populace. IntroductionĪs a result of its significant impact on people’s day-to-day lives around the globe, the emergence of COVID-19 sparked a global wave of anxiety and fear. ![]() The obtained accuracy results reached 78.062%, and this result outperforms many traditional ML classifiers based on TF-IDF applied on the same dataset and another model based on the CNN classifier. The model divides the dataset into training and testing and depends on the sentence similarity in detecting sentiment class. The sentence embedding model determines the meaning of word sequences instead of individual words. The dataset used in this research was collected from Twitter, and it was classified as positive, neutral, and negative. In this research article, we introduce a deep learning sentiment analysis model based on Universal Sentence Encoder. Using these tweets (sentiments) about the coronavirus and analyzing them in a computational model can help decision makers in measuring public opinion and yielding remarkable findings. In the period of the COVID-19 pandemic, a lot of people and media organizations were writing and documenting their health status and the latest news about the coronavirus on social media. The data on social sites describe many real-life events in our daily lives. One of the reasons is the appearance of social media. ![]() The spread of data on the web has increased in the last twenty years. ![]()
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