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).
Summary * Experience in Business
Processes Analysis and Design using OOAD Methodology and
tools * Strong experience in developing Telecom digital channel for major Marketing and promotion campaigns for emails, SMS and IVR * Experience in agile and waterfall methodologies sprint planning meetings, backlog grooming, points
estimation, sprint burn down / up charts, product backlogs, user stories and developing * acceptance crite...