Authors:
Nikolaos Nodarakis
1
;
Spyros Sioutas
2
;
Athanasios K. Tsakalidis
1
and
Giannis Tzimas
3
Affiliations:
1
University of Patras, Greece
;
2
Ionian University, Greece
;
3
Technological Educational Institute of Western Greece, Greece
Keyword(s):
Big Data, Bloom Filters, Classification, MapReduce, Hadoop, Sentiment Analysis, Twitter.
Related
Ontology
Subjects/Areas/Topics:
Distributed and Parallel Applications
;
Internet Technology
;
Web Information Systems and Technologies
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.