Twitter user classification with posting locations

Abstract

Twitter contains a large number of postings related to the reputation of products and services. Analyzing these data can provide useful marketing information. Inferring the user class would make it possible to extract opinions related to each class. In this paper, we propose a method that treats each user’s posting location for a tweet as a feature in the analysis of user classes. The proposed method creates clusters of geotags (obtained from Twitter tags) to identify the locations most often visited by the target user, which are then used as features. As an example, we conducted experiments to classify targets based on three classes: “student,” “working member of society,” and “housewife.” We obtained an average F-measure of 0.779, which represents an improvement on baseline results.