Prediction of Personality Traits in Twitter Users with Latent Features

The globalization of Economy has forced the society to maintain a constant evolution in marketing techniques. It is thus very important to design tools and methods that allow knowing and characterize individuals in groups to develop effective marketing strategies. In this context, any company would be interested in finding the tastes and preferences of people regarding the products and services offered in the global market. One technique that could help in this, is the analysis of the personality of each individual to identify their tastes and preferences. In this way we can offer products and services that meet their needs through appropriate advertising for each type of personality. In this work, we propose the use of latent features, extracted with a diversity of dimensionality reduction methods, to infer the personality of Twitter users using textual content-based features, and we compare the performance of the different techniques. For conducting our experiments, we use the PAN CLEF 2015 dataset consisting of 14,166 tweets in English of 152 different users, and a diversity of classification methods. Our results shows interesting insight about the personality prediction task.