# Exploring Pandemics Events on Twitter by Using Sentiment Analysis and Topic Modelling
## 2 Related Works  
Numerous studies have already explored the analysis of pandemic events on social media by using machine learning techniques and natural language processing. In the following, we are going to summarise studies that consider just one epidemic, such as COVID-19, Ebola, and influenza. [PERSON] et al. [7] use five machine learning algorithms (decision tree, logistic regression, k-nearest neighbours, random forest, and support vector machine) based on the historically labelled coronavirus tweets dataset to build a sentiment classifier. [PERSON] et al. [8] present a real time tool, COVIDSensing, in which they use topic modelling and a sentiment analysis to analyse the socio-economic problems related to COVID-19 on Twitter, Really Simple Syndication, and Telegram. Apart from the topic and sentiment, [PERSON] et al. [9] also assign labels, such as geolocation, named entities, user types, and gender, for a dataset with two billion multilingual tweets about COVID-19. In order to geotag tweets, they use five meta-data attributes: tweet text, user location, user profile description, geo-coordinates, and place tags by geocoding and reverse geocoding. In the named-entity recognition task, they use named-entity recognition (NER) models to recognise eighteen different types of entities. Based on the user type, the first names of the identified personal accounts are employed for training a supervised machine learning classifier to classify gender. When dealing with tweets with geolocation information, it is meaningful to estimate the users' mobility. For example, if the length from the location of the first tweet identifier to the location of the second tweet identifier is larger than 100 m, [PERSON] et al. [10] regard it as one trip. Then, they perform the same operation for all the users that published some messages on a specific day. [PERSON] et al. [11] present an interactive web platform to aggregate and visualise social media mining regarding COVID-19. They use the OntoGene Entity Recognizer for drug brand name detection. They detect URLs referring to preprint papers and estimate their popularity. In contrast, to address the general awareness of health issues, they use a Bidirectional Encoder Representation from Transformers (BERT)-based model to identify a personal health mention. [PERSON] et al. [12] explore the tweets spread about COVID-19 in Italy. They employ logistic regression and random forests to classify fake news or misinformation. Other researchers [13; 14] build models to identify the appearance of misinformation related to COVID-19 in different media. [PERSON] [15] focuses on the number of tweets and retweets related to Ebola and finds there is a correlation between electronic news media outlets and social media discussions. [PERSON] et al. (2016) analyse posts discussing avian influenza on Twitter. In detecting irrelevant tweets, they use an expectation-maximisation-based semi-supervised classifier to determine the class label of an unlabelled tweet. [PERSON] et al. (2017) detect influenza on Twitter by a support vector machine to classify the negative and positive influenza tweets. [PERSON] et al. (2018) combine multiple influenza-like illnesses (ILI) activity estimates into a single prediction of an ILI by machine learning techniques, such as stacked linear regression, a support vector machine with radial basis function kernels, and AdaBoost with decision trees regression. Twitter volumes can be regarded as a sign of real life. [PERSON] et al. (2019) create a relative increase indicator about the volume of tweets related to the vaccine of COVID-19 and investigated its tendency with real events. Apart from tweets, retweet interactions can reflect a real-life community structure ([PERSON] et al., 2018). Although replies and quotes can also express a certain meaning, they are still ambiguous as compared to retweets ([PERSON] et al., 2018).