Online Reputation Management
(ORM) is currently dominated by expert abilities. One of the great challenges is to effectively collect annotated training samples, especially to be able to generalize a small pool of expert feedback from area scale to a more global scale. One possible solution is to use advanced
(ML) techniques, to select annotations from training samples, and propagate effectively and concisely. We focus on the critical issue of understanding the different levels of annotations. Using the framework proposed by the RepLab contest we present a considerable number of experiments in Reputation Monitoring and Author Profiling. The proposed methods rely on a large variety of
Natural Language Processing
(NLP) methods exploiting tweet contents and some background contextual information. We show that simple algorithms only considering tweets content are effective against state-of-the-art techniques.