Title :
SentiMeter-Br: A new social web analysis metric to discover consumers´ sentiment
Author :
Renata Lopes Rosa;Demóstenes Zegarra Rodríguez;Graça Bressan
Author_Institution :
Department of Computer Science and Digital Systems - University of Sã
Abstract :
This article analyzes Brazilian Consumers´ Sentiments in a specific domain using a system, SentiMeter-Br. A Portuguese dictionary focused in a specific field of study was built, in which tenses and negative words are treated in a different way of other dictionaries, with a different metric. For the Portuguese dictionary performance validation, the results are compared with the SentiStrength algorithm 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.
Keywords :
"Dictionaries","Hair","Classification algorithms","Algorithm design and analysis","Market research","Twitter","Google"
Conference_Titel :
Consumer Electronics (ISCE), 2013 IEEE 17th International Symposium on
Print_ISBN :
978-1-4673-6198-9
DOI :
10.1109/ISCE.2013.6570158