Assigning Geo-relevance of Sentiments Mined from Location-Based Social Media Posts

Broad adoption of smartphones has increased the number of posts generated while people are going about their daily lives. Many of these posts are related to the location where that post is generated. Being able to infer a person’s sentiment toward a given location would be a boon to market researchers. The large percentage of system-generated content in these posts posed difficulties for calculating sentiment and assigning that sentiment to the location associated with the post. Consequently our proposed system implements a sequence of text cleaning functions which was completed with a naive Bayes classifier to determine if a post was more or less likely to be associated with an individual’s present location. The system was tested on set of nearly 30,000 posts from Foursquare that had been cross-posted to Twitter which resulted in reasonable precision but with a large number of posts discarded.