###### Abstract  
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. sentiment analysis; recurrent neural network; information extraction; named entity recognition; social media +
Footnote †: journal: Medical  
There are many options for executing sentiment analyses, from 'human categorization' to 'dictionary based'and 'deep learning' methods. In the field of tools, we can choose from fully ready-to-use tools, development kits, and completely custom-developed models. One such tool is 'TextBlob' ([[https://textblob.readthedocs.io/en/dev/](https://textblob.readthedocs.io/en/dev/)]([https://textblob.readthedocs.io/en/dev/](https://textblob.readthedocs.io/en/dev/)) accessed on 1 October 2021), which is fully ready to be integrated into any analysis--just import the library, and it is ready to use. As mentioned earlier, there are also options that allow us to create our own models, and build and train them based on our own data. 'Using Bidirectional encoder representations from transformers' (BERT) ([[https://github.com/google-research/bert](https://github.com/google-research/bert)]([https://github.com/google-research/bert](https://github.com/google-research/bert)) accessed on 1 October 2021) for sentiment analysis is one of the most powerful tools that we can use, but we can also create a 'Recurrent neural network' ([[https://developers.google.com/machine-learning/glossary/#recurrent_neural_network](https://developers.google.com/machine-learning/glossary/#recurrent_neural_network)]([https://developers.google.com/machine-learning/glossary/#recurrent_neural_network](https://developers.google.com/machine-learning/glossary/#recurrent_neural_network)) accessed on 1 October 2021) (RNN) or use the 'Natural Language Toolkit' ([[https://www.nltk.org/](https://www.nltk.org/)]([https://www.nltk.org/](https://www.nltk.org/)) accessed on 1 October 2021) (NLTK) with the VADER lexicon and SentimentIntensityAnalyzer. The main goal is to train a model to sentiment prediction by looking at correlations between words and tagging it to positive or negative sentiments. Thus, we created the RNN, BERT, NLTK-Vader lexicon models and imported the TextBlob tool into our analysis. We compared these primarily with the results of BERT.