# Exploring Pandemics Events on Twitter by Using Sentiment Analysis and Topic Modelling
## 10 Discussion and Conclusions  
In this work, we have used different natural language processing and machine learning techniques to explore the pandemics' information on Twitter social media. We have excluded COVID-19 in order to avoid having unbalanced data for the other viruses. The findings support us to answer our research questions. RQ1. Which pandemics have more discussion on social media? What is the trend of these discussions over time? Despite COVID-19 being excluded from our analysis, the collected data show that COVID-19 is accounted for by people's discussions according to the frequency of hashtags and topic modelling. Furthermore, discussions about malaria, influenza, and tuberculosis occur the most on Twitter, while the number of tweets related to typhus is the smallest one. We have also observed that malaria, influenza, and tuberculosis are the most popular according to the number of retweets, replies, and likes. From our understanding, the main reasons are: (1) the presence of two special days about malaria and tuberculosis; (2) influenza is a very general term and has many variations, such as Spanish flu and swine flu; and (3) nowadays, typhus is actually considered a rare disease [51]. RQ2. What are people's concerns related to these pandemics? Our study deals with this question from several different points of view. Firstly, by calculating the frequency of words, we have identified the top 30 words or hashtags to explore people's concerns. Secondly, after the vectorisation of each tweet message, we have computed the distance between the vectors to explore the observations' similarity by using \(k\)-means and then interpreted every cluster. Finally, according to the topic modelling, we have determined the latent topic for every tweet. We have understood that some people's concerns are related to the disease itself while others are related to politics and war. RQ3. What are their attitudes or emotions to these epidemics? \begin{table}
\begin{tabular}{c c c} \hline \hline
**Pandemic** & **Dominant Emotion** & **Topics** \\ \hline \multirow{7}{*}{Tuberculosis} & & Topic1—COVID-19 \\ \cline{3-3}  & & Topic2—Viral resistance to drugs \\ \cline{3-3}  & & Topic3—Other diseases, such as cancer and diabetes \\ \cline{3-3}  & & Topic4—Call to fight the virus \\ \cline{3-3}  & & Topic5—Achievement of treatment \\ \cline{3-3}  & & Topic6—Infection in the prison \\ \cline{3-3}  & & Topic1—World Tuberculosis Day to raise people’s awareness \\ \cline{3-3}  & & Topic2—New information from research \\ \cline{3-3}  & & Topic3—Medical system \\ \cline{3-3}  & & Topic4—Collaboration and campaign around the world \\ \hline \multirow{3}{*}{Typhus} & & Topic1—Deaths \\ \cline{3-3}  & & Topic2—The outbreak of disease and war \\ \cline{3-3}  & & Topic3—Vaccine \\ \hline \multirow{3}{*}{Yellow fever} & & Topic1—Vaccine \\ \cline{3-3}  & & Topic2—Outbreak in Kenya \\ \cline{3-3}  & & Topic3—Deaths \\ \hline \multirow{3}{*}{Zika} & & Topic1—Infection caused by mosquito \\ \cline{3-3}  & & Topic2—Dengue \\ \cline{3-3}  & & Topic3—The outbreak of Zika \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Topics for pandemic and emotion pair: tuberculosis, typhus, yellow fever, and Zika are listed with the fear emotion; tuberculosis also shows topics for the trust emotion. From the result of the sentiment analysis, fear, trust, and disgust are the three most dominant emotions. Specifically, we have presented the emotion distribution for every pandemic. RQ4. How can the mined information help or guide us in real life? According to the last section, we have found that people are scared of most of the pandemics. The frightening topics are often related to the cause of the pandemics and their influence on human beings and society. It is worth highlighting that people have a fear emotion to several medical treatments, such as wearing masks or taking a vaccine, although these measures are effective in controlling pandemics.