In the current age of Big Data, social media (e.g., Twitter, Facebook, Snapchat, Instagram) plays a dominant role as an everyday information exchange channel. Demographic information extracted from social media data is highly valued with its potential as an easy data collection method and the information it could provide to fields such as Marketing and Health Research (among many others). In the last few years, the social networking service Twitter has particularly gained a reputation in the research community as a data goldmine. (Ngyun et al., 2016)(Angwin, 2010)(Pak & Paroubek, 2010). Twitter is a microblogging platform - “a kind of blogging where users publish snippets of information about their daily activities and thoughts” (Zhang et al., 2010) - which has been described by Kottke (2015) as a “quick and dirty stream of consciousness”. Tweets are the opinions and thoughts of users expressed in real time which may serve as a rich data mine for demographic and product analyses. This generation of unabating self-expression has opened up a hot topic in the Geographic Information Sciences because GIS Technologies are effective in visualizing, managing, and analyzing big data. Using Topic Modelling as a word association-based data sorting method and then ArcMap to visualize and analyze the data, 690,000 geo-tagged tweets collected across the City of Vancouver were used to understand the distribution of happy and sad emotions in addition to food types consumed based on the notion that they are indicators of healthy and unhealthy eating habits (Blei, 2012). In doing so, we show how Twitter can be a cost-effective, efficient and alternative source of public health data in addition to exemplifying GIS as an effective data-enriching technology.