Multilingual sentiment analysis: from formal to informal and scarce resource languages.

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Title:	Multilingual sentiment analysis: from formal to informal and scarce resource languages.
Authors:	Lo, Siaw¹, Cambria, Erik² cambria@ntu.edu.sg, Chiong, Raymond¹, Cornforth, David¹
Source:	Artificial Intelligence Review. Dec2017, Vol. 48 Issue 4, p499-527. 29p.
Subjects:	Multilingual computing, Social media, Language & languages, Electronic information resources, Multilingualism
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 for dealing with scarce resource languages. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
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 for dealing with scarce resource languages. [ABSTRACT FROM AUTHOR]
ISSN:	02692821
DOI:	10.1007/s10462-016-9508-4