## 3 Literature Review
### Twitter Data Analytics in Healthcare
[71] reported that sentiment analysis of Twitter data revealed some of the unreported side effects for five drugs related to cancer medications. The researchers firstly specified the drug users' Twitter accounts using a Support Vector machine (SVM) classifier. Then, they evolved an analytic framework that integrates natural language processing and machine learning methods to capture drug-related adverse events from the Twitter messages. Their findings showed the possibility of supporting pharmacovigilance by extracting knowledge from tweets. [PERSON] and [PERSON] explored in [72] the role of Twitter data analysis in tobacco consumption surveillance. The extracted sentiment was linked in complex ways with social image, personal experience, and recently popular products such as the hookah and electronic cigarettes. It also showed the need for public awareness of their health effects. Taken together, these findings suggest a role for machine classification of tobacco-related posts over strictly keyword-based approaches in enhancing tobacco surveillance applications. [PERSON] et al. [73] investigated whether Twitter data can be a promising source for researchers to identify suicide-related risk factors. They indicated in their study that Twitter could be a useful tool for the early detection of individuals who are at risk of suicide. Their findings demonstrated a strong correlation between Twitter data and actual suicide data for a certain state in the United States. [PERSON] et al. [74] implemented a novel approach in estimating influenza statistics using Twitter data and real data provided by Centers for Disease Control and Prevention (CDC). Their model showed that to some extent, Twitter data could be conferred as a reliable source for the real-time assessment of epidemic conditions and could offset the lack of real statistics. They showed that text mining significantly improves the correlation between Twitter and the Influenza like Illness (ILI) rates provided by the Centers for Disease Control and Prevention (CDC). They built a model using a support vector machine algorithm and simple bag-of-words text processing to detect flu cases. Due to the high correlation of their model results with real statistics, they then built an estimation tool using the same techniques that could compensate for the absence of real statistical cases. Furthermore, for a certain disease such as influenza, surveillance approaches have proved their efficiency when tested on larger scale regions [75]. However, not only influenza was a public health concern; asthma is another concern that implies readiness from emergency rooms in hospitals. In [76], a robust model was developed using different sources of real data to predict the number of asthma-related emergency department (ED) visits in a specific area. Unlike the traditional surveillance tools, which rely on EMRs (electronic medical records), the study relied on employing machine learning in social media and environmental sensors data. In practice, for a specific geographic area within a time period, they collected Twitter data and examined the relationship between Twitter data,internet users' search interests from Google, ED asthma-related visits data, and pollution sensor data. The association between these different data was examined to build a prediction tool for asthma-related visits. After that, a prediction tool was built to estimate the number of ED asthma related visits. The tool successfully estimated the rate of asthma ED visits using a combination of independent variables from the above-mentioned data sources. [PERSON] in [77] developed a flu tracking system using a supervised learning approach for the analysis of flu-related tweets. The results of the tracking system showed a high correlation with the real statistics generated by the national health authorities. That provided further confidence in the usefulness of Twitter as a data resource for health-related research and for the robustness of natural language processing NLP algorithms.