One of the most hyped applications of big data analysis to social media is sentiment analysis (a.k.a. opinion mining). Sentiment analysis is the area of Natural Language Processing that aims to identify and extract subjective information from text. This generally includes identifying if a piece of text is subjective or objective, what sentiment (a.k.a. valence) it expresses (positive or negative), what emotion it conveys and towards which entity or aspect of the text. Companies and marketers are mostly interested in automatically inferring public opinion about products, movies or actions.
Opposite to mining these attitudes towards other objects, people also express their own emotions online. We decided to analyze this less popular facet: learning about the emotions of people posting subjective messages. In this post I’ll present variations in sentiment and intensity of Facebook posts and how these vary with the attributes of the people that post them. I will investigate a number of user traits such as gender, age and personality.