Paper: TJP: Using Twitter to Analyze the Polarity of Contexts

ACL ID S13-2061
Title TJP: Using Twitter to Analyze the Polarity of Contexts
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2013
Authors

This paper presents our system, TJP, which participated in SemEval 2013 Task 2 part A: Contextual Polarity Disambiguation. The goal of this task is to predict whether marked con- texts are positive, neutral or negative. Howev- er, only the scores of positive and negative class will be used to calculate the evaluation result using F-score. We chose to work as ?constrained?, which used only the provided training and development data without addi- tional sentiment annotated resources. Our ap- proach considered unigram, bigram and trigram using Na?ve Bayes training model with the objective of establishing a simple- approach baseline. Our system achieved F- score 81.23% and F-score 78.16% in the re- sults for SMS messages and Tweets respec- tively.