CMUQ$@$Qatar:Using Rich Lexical Features for Sentiment Analysis on Twitter
Sabih Bin Wasi, Rukhsar Neyaz, Houda Bouamor, Behrang Mohit · 2014
In this paper, we describe our system for the Sentiment Analysis of Twitter shared task in SemEval 2014. Our system uses an SVM classifier along with rich set of lexical features to detect the sentiment of a phrase within a tweet (Task-A) and also the sentiment of the whole tweet (TaskB). We start from the lexical features that were used in the 2013 shared tasks, we enhance the underlying lexicon and also introduce new features. We focus our feature engineering effort mainly on TaskA. Moreover, we adapt our initial framework and introduce new features for TaskB. Our system reaches weighted score of 87.11% in Task-A and 64.52% in Task-B. This places us in the 4th rank in the TaskA and 15th in the Task-B.