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SentiMeter-Br: Facebook and Twitter Analysis Tool to Discover Consumers' Sentiment

Authors:
Renata Lopes Rosa
Demóstenes Zegarra Rodríguez
Graça Bressan

Keywords: consumer sentiment; Twitter; Facebook; machine learning; social web analysis tool; support vector machines;

Abstract:
Brazilian Consumers' Sentiments are analyzes in a specific domain using a system, SentiMeter-Br. A Portuguese dictionary focused on a specific field of study was built, in which tenses and negative words are treated in a different way to measure the polarity, the strength of positive or negative sentiment, in short texts extracted from Twitter. For the Portuguese dictionary performance validation, the results are compared with the SentiStrength tool and are evaluated by three Specialists in the field of study; each one analyzed 2000 texts captured from Twitter. Comparing the efficiency of the SentiMeter-Br and the SentiStrength against the Specialists' opinion, a Pearson correlation factor of 0.89 and 0.75 was reached, respectively. The polarity of the short texts were also tested through machine learning, with correctly classified instances of 71.79% by Sequential Minimal Optimization algorithm and F-Measure of 0.87 for positive and 0.91 for negative phrases. Another contribution is a Twitter and Facebook search framework that extracts online tweets and Facebook posts, the latter with geographic location, gender and birthdate of the user who posted the comments, and can be accessed by mobile phones.

Pages: 60 to 65

Copyright: Copyright (c) IARIA, 2013

Publication date: June 23, 2013

Published in: conference

ISSN: 2308-4030

ISBN: 978-1-61208-279-0

Location: Rome, Italy

Dates: from June 23, 2013 to June 28, 2013