Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/41590
metadata.artigo.dc.title: BigFeel-A distributed processing environment for the integration of sentiment analysis methods
metadata.artigo.dc.creator: Ferreira, Roger Santos
Pereira, Denilson Alves
metadata.artigo.dc.subject: Sentiment analysis
Apache Spark
Hadoop
Big data
Análise de sentimento
Aprendizagem de máquina
Processamento de linguagem natural
metadata.artigo.dc.publisher: The British Computer Society
metadata.artigo.dc.date.issued: 2019
metadata.artigo.dc.identifier.citation: FERREIRA, R. S.; PEREIRA, D. A. BigFeel-A distributed processing environment for the integration of sentiment analysis methods. The Computer Journal, London, v. 62, n. 11, p. 1671-1683, Nov. 2019.
metadata.artigo.dc.description.abstract: Sentiment analysis has been the main focus of plenty of research efforts, particularly justified by its commercial significance, both for consumers and businesses. Thus, many methods have been proposed so far, and the most prominent have been compared in terms of effectiveness. Nonetheless, the literature is deficient when it comes to assessing the efficiency of these methods for processing large volumes of data. In this study, we performed an experimental assessment of the efficiency of 22 methods in total, whose implementations were available. We also proposed and assessed an environment for distributed processing methods for sentiment analysis, using the Apache Spark platform, named BigFeel. In this environment, the existing methods, outlined to run in a non-distributed way, can be adapted, without altering their source code, to run in a distributed manner. The experimental results reveal that (i) few methods are efficient in their native form, (ii) the methods improve their efficiency after having been integrated into BigFeel, (iii) some of them, which were unfeasible to process a large dataset, became viable when deployed in a computer cluster and (iv) some methods can only handle small datasets, even in a distributed manner.
metadata.artigo.dc.identifier.uri: https://academic.oup.com/comjnl/article-abstract/62/11/1671/5389528
http://repositorio.ufla.br/jspui/handle/1/41590
metadata.artigo.dc.language: en
Appears in Collections:DCC - Artigos publicados em periódicos

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