## 2 Related Works
### Information Extraction and Named Entity Recognition  
Automating clinical de-identification through deep learning techniques has been shown to be less effective in languages other than English, due to dataset scarcity. Therefore, a new Italian identification data set was created from the COVID-19 clinical records provided by the Italian Radiological Society (SIRM). Two multilingual in-depth learning systems have been developed [25] for this low-resource language scenario to investigate their ability to transfer knowledge between different languages, while maintaining the necessary features to correctly perform the Named Entity Recognition task for de-identification.  
The development of COVID-19 automated detection systems based on natural language processing (NLP) techniques can be a huge help to support clinicians and detect COVID-19-related abnormalities in radiological reports. In [26], the authors propose a text classification system based on the integration of different sources of information. The system can be used to automatically predict whether or not a patient has radiological findings consistent with COVID-19, on the basis of radiological reports of chest CT. To train the text classification system, they apply machine learning approaches and named entity recognition.  
The authors [27] created this CORD-NER dataset with comprehensive named entity recognition (NER) on the COVID-19 Open Research Dataset Challenge (CORD-19) corpus, which covers many new entity types related to COVID-19. CORD-NER annotation is a combination of four sources with different NER methods.  
Free-text clinical notes can contain critical information to address different issues. So, we need data-driven, automatic information extraction models to use this text-encoded information in large-scale studies. Ref. [28] introduces a new clinical corpus, called the COVID-19 Annotated Clinical Text (CACT) corpus, which contains 1472 notes, with detailed notes describing the diagnosis, examination, and clinical presentation of COVID-19. The authors presented a span-based event extraction model that collectively extracts all observed phenomena and achieves high performance in identifying COVID-19 and symptom events with associated assertion values.  
The huge amount of unstructured free-form text in medical records is a major barrier. An information extraction-based approach has been described by the authors [29], which automatically converts unstructured text into structured data, which is cross-referenced against eligibility criteria, using a rule-based system to determine which patients qualify for a major HFpEF clinical trial.  
With X-ray images from patients with common bacterial pneumonia, confirmed COVID-19, and normal incidents, were utilized for the automatic detection of COVID-19.  
The aim of the authors [30] was to evaluate the performance of state-of-the-art convolutional neural network architectures proposed in recent years for medical image classification.  
Besides the works discussed above, there are many other methods of sentiment analysis and data analysis. In this paper, we compare the results of sentiment analysis models, which was listed earlier (TextBlob, NLTK, RNN, BERT), and then perform further analyses on the labelled data from RNN model to explore and explain this result in more depth. Such a comparison and further analysis have not been discussed in the related works.