Improved lexicon-based sentiment analysis for social media analytics

Research output: Contribution to journalArticle

Abstract

Social media channels, such as Facebook or Twitter, allow for people to express their views and opinions about any public topics. Public sentiment related to future events, such as demonstrations or parades, indicate public atti-tude and therefore may be applied while trying to estimate the level of disruption and disorder during such events. Consequently, sentiment analysis of social media content may be of interest for di erent organisations, especially in security and law enforcement sectors. This paper presents a new lexicon-based sentiment analysis algorithm that has been designed with the main focus on real time Twitter content analysis. The algorithm consists of two key compo- nents, namely sentiment normalisation and evidence-based combination function, which have been used in order to estimate the intensity of the sentiment rather than positive/negative label and to support the mixed sentiment classi cation process. Finally, we illustrate a case study examining the relation between negative sentiment of twitter posts related to English Defence League and the level of disorder during the organisation’s related events.
LanguageEnglish
Pages1-13
JournalSecurity Informatics
Volume4
Issue number9
DOIs
Publication statusPublished - Dec 2015

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Law enforcement
Labels
Demonstrations
Positive ions

Keywords

  • Sentiment analysis
  • Social media
  • Security

Cite this

@article{4026fbb194a54e5e90b084bc1790e275,
title = "Improved lexicon-based sentiment analysis for social media analytics",
abstract = "Social media channels, such as Facebook or Twitter, allow for people to express their views and opinions about any public topics. Public sentiment related to future events, such as demonstrations or parades, indicate public atti-tude and therefore may be applied while trying to estimate the level of disruption and disorder during such events. Consequently, sentiment analysis of social media content may be of interest for di erent organisations, especially in security and law enforcement sectors. This paper presents a new lexicon-based sentiment analysis algorithm that has been designed with the main focus on real time Twitter content analysis. The algorithm consists of two key compo- nents, namely sentiment normalisation and evidence-based combination function, which have been used in order to estimate the intensity of the sentiment rather than positive/negative label and to support the mixed sentiment classi cation process. Finally, we illustrate a case study examining the relation between negative sentiment of twitter posts related to English Defence League and the level of disorder during the organisation’s related events.",
keywords = "Sentiment analysis, Social media, Security",
author = "Anna Jurek and Maurice Mulvenna and Yaxin Bi",
year = "2015",
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language = "English",
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}

Improved lexicon-based sentiment analysis for social media analytics. / Jurek, Anna; Mulvenna, Maurice; Bi, Yaxin.

In: Security Informatics, Vol. 4, No. 9, 12.2015, p. 1-13.

Research output: Contribution to journalArticle

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AU - Jurek, Anna

AU - Mulvenna, Maurice

AU - Bi, Yaxin

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AB - Social media channels, such as Facebook or Twitter, allow for people to express their views and opinions about any public topics. Public sentiment related to future events, such as demonstrations or parades, indicate public atti-tude and therefore may be applied while trying to estimate the level of disruption and disorder during such events. Consequently, sentiment analysis of social media content may be of interest for di erent organisations, especially in security and law enforcement sectors. This paper presents a new lexicon-based sentiment analysis algorithm that has been designed with the main focus on real time Twitter content analysis. The algorithm consists of two key compo- nents, namely sentiment normalisation and evidence-based combination function, which have been used in order to estimate the intensity of the sentiment rather than positive/negative label and to support the mixed sentiment classi cation process. Finally, we illustrate a case study examining the relation between negative sentiment of twitter posts related to English Defence League and the level of disorder during the organisation’s related events.

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