n-grams

In this paper we describe the system submitted for the SemEval 2014 Task 9 (Sentiment Analysis in Twitter) Subtask B. Our contribution consists of a supervised approach using machine learning techniques, which uses the terms in the dataset as features. In this work we do not employ any external knowledge and resources. The novelty of our approach lies in the use of words, ngrams and skipgrams (not-adjacent ngrams) as features, and how they are weighted.
 

In this paper, we present our contribution for the Task 1 (6 levels polarity classification) of the TASS 2013 competition. This contribution consists on two different approaches: a modified version of a ranking algorithm (RA-SR) using bigrams, and new proposal using a skipgrams scorer. These approaches create sentiment lexicons able to retain the context of the terms. All our approaches appear in the top 10 best results of the systems presented to the competition, and the combination of them reaches the first position.