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Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Multilingual Sentiment Analysis: State of the Art and Independent Comparison of Techniques
Author(s): Dashtipour, Kia
Poria, Soujanya
Hussain, Amir
Cambria, Erik
Hawalah, Ahmad Y A
Gelbukh, Alexander
Zhou, Qiang
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Keywords: Artificial intelligence
Natural language processing
Opinion mining
Sentic computing
Sentiment Analysis
Issue Date: Aug-2016
Date Deposited: 3-Jun-2016
Citation: Dashtipour K, Poria S, Hussain A, Cambria E, Hawalah AYA, Gelbukh A & Zhou Q (2016) Multilingual Sentiment Analysis: State of the Art and Independent Comparison of Techniques. Cognitive Computation, 8 (4), pp. 757-771.
Abstract: With the advent of Internet, people actively express their opinions about products, services, events, political parties, etc., in social media, blogs, and website comments. The amount of research work on sentiment analysis is growing explosively. However, the majority of research efforts are devoted to English-language data, while a great share of information is available in other languages. We present a state-of-the-art review on multilingual sentiment analysis. More importantly, we compare our own implementation of existing approaches on common data. Precision observed in our experiments is typically lower than the one reported by the original authors, which we attribute to the lack of detail in the original presentation of those approaches. Thus, we compare the existing works by what they really offer to the reader, including whether they allow for accurate implementation and for reliable reproduction of the reported results.
DOI Link: 10.1007/s12559-016-9415-7
Rights: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, 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.
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