Please use this identifier to cite or link to this item: http://www.repositorio.ufop.br/handle/123456789/9265
Title: SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods.
Authors: Ribeiro, Filipe Nunes
Araújo, Matheus
Gonçalves, Pollyanna
Gonçalves, Marcos André
Souza, Fabrício Benevenuto de
Keywords: Sentiment analysis
Benchmark
Methods evaluation
Issue Date: 2016
Citation: RIBEIRO, F. N. et al. SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods. EPJ Data Science, v. 5, p. 1-29, 2016. Disponível em: <https://epjdatascience.springeropen.com/track/pdf/10.1140/epjds/s13688-016-0085-1?site=epjdatascience.springeropen.com>. Acesso em: 02 out. 2017.
Abstract: In the last few years thousands of scientific papers have investigated sentiment analysis, several startups that measure opinions on real data have emerged and a number of innovative products related to this theme have been developed. There are multiple methods for measuring sentiments, including lexical-based and supervised machine learning methods. Despite the vast interest on the theme and wide popularity of some methods, it is unclear which one is better for identifying the polarity (i.e., positive or negative) of a message. Accordingly, there is a strong need to conduct a thorough apple-to-apple comparison of sentiment analysis methods, as they are used in practice, across multiple datasets originated from different data sources. Such a comparison is key for understanding the potential limitations, advantages, and disadvantages of popular methods. This article aims at filling this gap by presenting a benchmark comparison of twenty-four popular sentiment analysis methods (which we call the state-of-the-practice methods). Our evaluation is based on a benchmark of eighteen labeled datasets, covering messages posted on social networks, movie and product reviews, as well as opinions and comments in news articles. Our results highlight the extent to which the prediction performance of these methods varies considerably across datasets. Aiming at boosting the development of this research area, we open the methods’ codes and datasets used in this article, deploying them in a benchmark system, which provides an open API for accessing and comparing sentence-level sentiment analysis methods.
URI: http://www.repositorio.ufop.br/handle/123456789/9265
ISSN: 21931127
metadata.dc.rights.license: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Fonte o próprio artigo.
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