NileTMRG at SemEval-2016 Task 7: Deriving prior polarities for Arabic sentiment terms
This paper presents a model that was developed to address SemEval Task 7: "Determining Sentiment Intensity of English and Arabic Phrases", with focus on 'Arabic Phrases'. The goal of this task is to determine the degree to which some given term is associated with positive sentiment. The underlying premise behind the model that we have adopted is that determining the context (positive or negative) in which a term usually occurs can determine its strength. Since the focus is on Twitter terms, Twitter was used to collect tweets for each term for which a strength value was to be derived. An Arabic sentiment analyzer, was then used to assign a polarity to each of these tweets, thus defining their context. We then experimented with normalized point wise mutual information with and without linear regression to assign intensity scores to input terms. The output of the model that we've adopted ranked at two out of the three presented systems for this task with a Kendall score of 0.475. © 2016 Association for Computational Linguistics.