In this paper, we describe our system that participated in SemEval-2013, Task 2.B (sentiment analysis in Twitter). Our approach consists of adapting Naive Bayes probabilities in order to take into account prior knowledge (represented in the form of a sentiment lexicon). We propose two different methods to efficiently incorporate prior knowledge. We show that our approach outperforms the classical Naive Bayes method and shows competitive results with SVM while having less computational complexity.
['Mohamed Dermouche', 'Leila Khouas', 'Julien Velcin', 'Sabine Loudcher']
AMI&ERIC: How to Learn with Naive Bayes and Prior Knowledge: an Application to Sentiment Analysis