Publication Type

Journal Article

Version

publishedVersion

Publication Date

12-2017

Abstract

The ability to analyse online user-generated content related to sentiments (e.g., thoughts and opinions) on products or policies has become a de-facto skillset for many companies and organisations. Besides the challenge of understanding formal textual content, it is also necessary to take into consideration the informal and mixed linguistic nature of online social media languages, which are often coupled with localised slang as a way to express ‘true’ feelings. Due to the multilingual nature of social media data, analysis based on a single official language may carry the risk of not capturing the overall sentiment of online content. While efforts have been made to understand multilingual sentiment analysis based on a range of informal languages, no significant electronic resource has been built for these localised languages. This paper reviews the various current approaches and tools used for multilingual sentiment analysis, identifies challenges along this line of research, and provides several recommendations including a framework that is particularly applicable to dealing with scarce resource languages.

Keywords

multilingual analysis, sentiment analysis, scarce resource languages, social media

Discipline

Artificial Intelligence and Robotics | Numerical Analysis and Scientific Computing | Social Media

Research Areas

Data Science and Engineering

Publication

Artificial Intelligence Review

Volume

48

Issue

4

First Page

499

Last Page

527

ISSN

0269-2821

Identifier

10.1007/s10462-016-9508-4

Publisher

Springer

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1007/s10462-016-9508-4

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