UMCC_DLSI-(SA): Using a ranking algorithm and informal features to solve Sentiment Analysis in Twitter

In this paper, we describe the development and performance of the supervised system UMCC_DLSI-(SA). This system uses corpora where phrases are annotated as Positive, Negative, Objective, and Neutral, to achieve new sentiment resources involving word dictionaries with their associated polarity. As a result, new sentiment inventories are obtained and applied in conjunction with detected informal patterns, to tackle the challenges posted in Task 2b of the Semeval2013 competition. Assessing the effectiveness of our application in sentiment classification, we obtained a 69% F-Measure for neutral and an average of 43% F-Measure for positive and negative using Tweets and SMS messages.