ŞEHİR e-arşiv

Scalable sentiment analytics

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dc.contributor.author Bakırov, Aslan
dc.contributor.author Çoğalmış, Kevser Nur
dc.contributor.author Bulut, Ahmet
dc.date.accessioned 2016-08-12T13:42:27Z
dc.date.available 2016-08-12T13:42:27Z
dc.date.issued 2016
dc.identifier.citation Bakırov, Aslan, Çoğalmış, Kevser Nur, Bulut, Ahmet. (2016). Scalable sentiment analytics. Turkish Journal of Electrical Engineering & Computer Sciences, 24, pp. 1560-1570. en_US
dc.identifier.uri http://hdl.handle.net/11498/32065
dc.description.abstract Spark has become a widely popular analytics framework that provides an implementation of the equally popular MapReduce programming model. Hadoop is an Apache foundation framework that can be used for processing large datasets on a cluster of computers using the MapReduce programming model. Mahout is an Apache foundation project developed for building scalable machine learning libraries, which includes built-in machine learning classifiers. In this paper, we show how to build a simple text classifier on Spark, Apache Hadoop, and Apache Mahout for extracting out sentiments from a text collection containing millions of text documents. Using a collection of 7 million movie reviews taken from IMDB, a Bayesian classifier was learned to predict sentiments for test reviews. Separate classifiers were learned on both Spark and Hadoop, i.e. our contenders for scalable sentiment analytics. Our empirical results showed that the sentiment learning task on Spark ran almost 10 times faster than the learning task on Hadoop. en_US
dc.language.iso eng en_US
dc.publisher TÜBİTAK en_US
dc.relation.isversionof 10.3906/elk-1311-128 en_US
dc.rights info:eu-repo/semantics/embargoedAccess en_US
dc.subject Sentiment Analysis en_US
dc.subject MapReduce en_US
dc.subject Spark en_US
dc.subject Hadoop en_US
dc.subject Duygu Analizi en_US
dc.title Scalable sentiment analytics en_US
dc.type Article en_US
dc.contributor.authorID TR246779 en_US
dc.contributor.authorID TR33361 en_US
dc.relation.journal Turkish Journal of Electrical Engineering & Computer Sciences en_US
dc.contributor.department İstanbul Şehir University. College of Engineering and Natural Sciences. Department of Computer Science. en_US
dc.identifier.volume 24 en_US
dc.identifier.endpage 1570 en_US
dc.identifier.startpage 1560 en_US

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