We differentiated between computational approaches (either based on volume data, such as the number of mentions related to a party or candidate or the occurrence of particular hashtags; or endorsement data, such as the number of Twitter followers, Facebook friends or the number of «likes» received on Facebook walls), sentiment
analysis approaches, that pay attention to the
language and try to attach a qualitative meaning to the comments (posts, tweets) published by social media users employing
automated tools for sentiment
analysis (i.e., via natural
language processing models or the employment of pre-defined ontological dictionaries), and finally what we call supervised and aggregated sentiment
analysis (SASA), that is, techniques that exploit the human codification in their process and focus on the estimation of the aggregated distribution of the opinions, rather than on individual classification of each single text (Ceron et al. 2016).