Editor-in-chief

Prof. Paul Sudnik

Munich University of Applied Sciences, Germany

As the Editor-in-Chief of IJSSH, I invite you to contribute your scholarly work to our esteemed publication. The journal publishes papers which focus on the advanced researches in the field of all aspects of social science and humanity. I'll endeavour to make this journal grow better and hopefully it will become a recognized journal among researchers and scholars in related fields.

Home > Archive > 2016 > Volume 6 Number 3 (Mar. 2016)
IJSSH 2016 Vol.6(3): 179-183 ISSN: 2010-3646
DOI: 10.7763/IJSSH.2016.V6.640

Social Data Analysis of Brazilian’s Mood from Twitter

David N. Prata, Kleber P. Soares, Michel A. Silva, Daniela Q. Trevisan, and Patrick Letouze

Abstract—In this work, a software application was developed to analyze and visualize messages over Twitter social network, ranking the posts relatively to variations in moods within the Brazilian territory. Artificial intelligence techniques such as text mining and sentiment analysis were used for this purpose. The use of methods of machine learning allows determining the polarity (positive or negative) of tweets collected. Results were displayed in cartograms, through representations of tweet's geographic locations. Surprisingly, another study of twitter’s mood from United States Nation showed similar results for the variation of moods throughout the day, hypothesizing a humor pattern for human beings during the period of 24 hours.

Index Terms—Twitter, sentiment analysis, social data mining.

David N. Prata, Kleber P. Soares, Michel A. Silva, Daniela Q. Trevisan, and Patrick Letouze are with the Department of Computer Science of the Federal University of Tocantins – “Universidade Federal do Tocantins”, Palmas-TO, 77.001-009 Brazil (e-mail: ddnprata@uft.edu.br).

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Cite: David N. Prata, Kleber P. Soares, Michel A. Silva, Daniela Q. Trevisan, and Patrick Letouze, " Social Data Analysis of Brazilian’s Mood from Twitter," International Journal of Social Science and Humanity vol. 6, no. 3, pp. 179-183, 2016.

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