MR-SAT: A MapReduce Algorithm for Big Data Sentiment Analysis on Twitter

Nikolaos Nodarakis, Spyros Sioutas, Athanasios K. Tsakalidis, Giannis Tzimas

2016

Abstract

Sentiment analysis on Twitter data has attracted much attention recently. People tend to express their feelings freely, which makes Twitter an ideal source for accumulating a vast amount of opinions towards a wide diversity of topics. In this paper, we develop a novel method to harvest sentiment knowledge in the MapReduce framework. Our algorithm exploits the hashtags and emoticons inside a tweet, as sentiment labels, and proceeds to a classification procedure of diverse sentiment types in a parallel and distributed manner. Moreover, we utilize Bloom filters to compact the storage size of intermediate data and boost the performance of our algorithm. Through an extensive experimental evaluation, we prove that our solution is efficient, robust and scalable and confirm the quality of our sentiment identification.

Paper Citation

in Harvard Style

Nodarakis N., Sioutas S., Tsakalidis A. and Tzimas G. (2016). MR-SAT: A MapReduce Algorithm for Big Data Sentiment Analysis on Twitter . In Proceedings of the 12th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-758-186-1, pages 140-147. DOI: 10.5220/0005850401400147

in Bibtex Style

@conference{webist16,
author={Nikolaos Nodarakis and Spyros Sioutas and Athanasios K. Tsakalidis and Giannis Tzimas},
title={MR-SAT: A MapReduce Algorithm for Big Data Sentiment Analysis on Twitter},
booktitle={Proceedings of the 12th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2016},
pages={140-147},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005850401400147},
isbn={978-989-758-186-1},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - MR-SAT: A MapReduce Algorithm for Big Data Sentiment Analysis on Twitter
SN - 978-989-758-186-1
AU - Nodarakis N.
AU - Sioutas S.
AU - Tsakalidis A.
AU - Tzimas G.
PY - 2016
SP - 140
EP - 147
DO - 10.5220/0005850401400147