Information Extraction and Named Entity Recognition Supported Social Media Sentiment Analysis during the COVID-19 Pandemic

Social media platforms are increasingly being used to communicate information, something which has only intensified during the pandemic. News portals and governments are also increasing attention to digital communications, announcements and response or reaction monitoring. Twitter, as one of the largest social networking sites, which has become even more important in the communication of information during the pandemic, provides space for a lot of different opinions and news, with many discussions as well. In this paper, we look at the sentiments of people and we use tweets to determine how people have related to COVID-19 over a given period of time. These sentiment analyses are augmented with information extraction and named entity recognition to get an even more comprehensive picture. The sentiment analysis is based on the 'Bidirectional encoder representations from transformers' (BERT) model, which is the basic measurement model for the comparisons. We consider BERT as the baseline and compare the results with the RNN, NLTK and TextBlob sentiment analyses. The RNN results are significantly closer to the benchmark results given by BERT, both models are able to categorize all tweets without a single tweet fall into the neutral category. Then, via a deeper analysis of these results, we can get an even more concise picture of people's emotional state in the given period of time. The data from these analyses further support the emotional categories, and provide a deeper understanding that can provide a solid starting point for other disciplines as well, such as linguistics or psychology. Thus, the sentiment analysis, supplemented with information extraction and named entity recognition analyses, can provide a supported and deeply explored picture of specific sentiment categories and user attitudes.