Sentiment Analysis Of Posts Of Social Media Users In Their Socially Important Locations

Socially important locations are frequently visited locations of social media users in their social media lifetime. Socially important locations reveal spatial preferences of social media users in a social media user group. Discovering socially important locations is important for recommending locations, marketing, urban planning, etc. However, the motivation of preferring these locations is unclear. This study is performed to reveal the motivation of location preferences of social media users. Polarity of socially important locations of social media users are detected using two popular document classification methods namely, SVM and Naive Bayes. The results showed that SVM algorithm is more efficient than Naive Bayes algorithm on detecting tweet polarities. Also, the location preference motivations could be revealed based on dominant polarity ratio.