Social sensing and sentiment analysis: Using social media as useful information source

In this paper, we briefly introduce the social sensing and sentiment analysis paradigms. The first one regards the general framework in which information coming from social media, and in particular from On Line Social Networks (OSNs), may be used for mining useful knowledge to be exploited in several real-world applications. Indeed, all the elements that people share on OSNs (texts, links, positions, images and so on) may be considered as the informative content of social or human sensors. This content may be used in several contexts such as real-time monitoring, prediction and identification of events, and for studying opinions, sentiments, moods and emotions that people share in the texts published on OSNs. The more specific framework, in which information coming from social networks are adopted for detecting the polarity (e.g., positive, neutral, or negative) of the sentiment associated with a text, is labelled as sentiment analysis. In this work, we also show two real-world applications of both social sensing and sentiment analysis.