Paper: AMI&ERIC: How to Learn with Naive Bayes and Prior Knowledge: an Application to Sentiment Analysis

ACL ID S13-2059
Title AMI&ERIC: How to Learn with Naive Bayes and Prior Knowledge: an Application to Sentiment Analysis
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2013
Authors

In this paper, we describe our system that par- ticipated in SemEval-2013, Task 2.B (senti- ment analysis in Twitter). Our approach con- sists of adapting Naive Bayes probabilities in order to take into account prior knowledge (represented in the form of a sentiment lex- icon). We propose two different methods to efficiently incorporate prior knowledge. We show that our approach outperforms the clas- sical Naive Bayes method and shows compet- itive results with SVM while having less com- putational complexity.