The huge and ever increasing amount of text generated by Twitter users everyday embeds a wealth of information, in particular, about themes that become suddenly relevant to many users as well as about the sentiment polarity that users tend to associate with these themes. In this paper, we exploit both these opportunities and propose a method for: (i) detecting novel popular themes, i.e. events, (ii) summarizing these events by means of a concise yet meaningful representation, and (iii) assessing the prevalent sentiment polarity associated with each event, i.e., positive vs. negative.
Our method is fully unsupervised and requires only a precompiled topic description in the form of set of potentially relevant keywords that might appear in the events of interest.
We validate our proposal on a real corpus of about 8,000,000 tweets, by detecting, classifying and summarizing events related to three wide topics associated with tech-related brands.