Tweester at SemEval-2017 Task 4: Fusion of Semantic-Affective and pairwise classification models for sentiment analysis in Twitter

Abstract

In this paper, we describe our submission to SemEval2017 Task 4: Sentiment Analysis in Twitter. Specifically the proposed system participated both to tweet polarity classification (two-, three- and five class) and tweet quantification (two and five-class) tasks. The submitted system is based on “Tweester” (Palogiannidi et al., 2016) that participated in last year's Sentiment analysis in Twitter Tasks A and B. Specifically it comprises of multiple independent models such as neural networks, semantic-affective models and affective models inspired by topic modeling that are combined in a late fusion scheme.