2017
DOI: 10.1504/ijima.2017.10006719
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Competitive analysis of social media data in the banking industry

Abstract: Abstract:Recently, most companies interact more with their customers through the social media, particularly Facebook and Twitter. This has made large amount of textual data freely available on the internet for competitive intelligence analysis, which is helping reposition more and more companies for better profit. In order to carry out competitive intelligence, financial institutions need to take note of and analyse their competitor's social media sites. This paper, therefore, aims to help the banking industry… Show more

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Cited by 5 publications
(3 citation statements)
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“…Textual and content analysis aims to identify the dimensions and mainstream concepts of empowerment within a text and provide insights into how comments and posts might be influenced by the commonly occurring patterns of meaning. Afolabi et al (2017) carried out a competitive analysis of social media data in banks and discovered that social media text analysis provides important competitive advantage trends; this is also a useful source of information for banks' management.…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…Textual and content analysis aims to identify the dimensions and mainstream concepts of empowerment within a text and provide insights into how comments and posts might be influenced by the commonly occurring patterns of meaning. Afolabi et al (2017) carried out a competitive analysis of social media data in banks and discovered that social media text analysis provides important competitive advantage trends; this is also a useful source of information for banks' management.…”
Section: Methodsmentioning
confidence: 99%
“…However, the focus of most of these studies has been mainly related to the banks and they seem to be concerned solely with marketing. Afolabi, Ezenwoke, and Ayo (2017) analysed social media data from five financial institutions in Nigeria, mainly to help banks identify competitive patterns and relationships derived from online data. Before that, Thoumrungroje (2014) empirically tested the impact of social media on 1,142 Thai consumers' trust in the 'electronic word of mouth' and their consumption of the advertised products.…”
Section: Introduction 11 Backgroundmentioning
confidence: 99%
“…The accuracy of algorithm is process on each movie genre namely action, adventure, drama and romantic where from the experiment, LSVM shows a higher accuracy compared to NB in all genres mention with the highest accuracy in drama type of movie. Furthermore, Ayo et al [9] used RapidMiner to combine tweets and comments on Facebook from five major banks in Nigeria. The result is divided into two parts which is by using sentiment analysis and clustering analysis.…”
Section: Introductionmentioning
confidence: 99%