  Twitter social network contains a large amount of information generated by
its users. That information is composed of opinions and comments that may
reflect trends in social behavior. There is talk of trend when it is possible
to identify opinions and comments geared towards the same shared by a lot of
people direction. To determine if two or more written opinions share the same
address, techniques Natural Language Processing (NLP) are used. This paper
proposes a methodology for predicting reflected in Twitter from the use of
sentiment analysis functions NLP based on social behaviors. The case study was
selected the 2015 Presidential in Argentina, and a software architecture Big
Data composed Vertica data base with the component called Pulse was used.
Through the analysis it was possible to detect trends in voting intentions with
regard to the presidential candidates, achieving greater accuracy in predicting
that achieved with traditional systems surveys.
