DAEDALUS at RepLab 2012: Polarity Classification and Filtering on Twitter Data

Abstract

This paper describes our participation at the RepLab 2012 profiling scenario, in both polarity classification and filtering subtasks. Our approach is based on 1) the information provided by a semantic model that includes rules and resources annotated for sentiment analysis, 2) a detailed morphosyntactic analysis of the input text that allows to lemmatize and divide the text into segments to be able to control the scope of semantic units and perform a fine- grained detection of negation in clauses, and 3) the use of an aggregation algorithm to calculate the global polarity value of the text based on the local polarity values of the different segments, which includes an outlier filter. The system, experiments and results are presented and discussed in the paper.