Social Media Analytics Using Business Intelligence and Social Media Tools – Differences and Implications

2017 ◽  
pp. 252-259
Author(s):  
Matthias Wittwer ◽  
Olaf Reinhold ◽  
Rainer Alt ◽  
Finn Jessen ◽  
Richard Stüber
Keyword(s):  
Social Media ◽  
Business Intelligence ◽  
Social Media Analytics ◽  
Social Media Tools

Social media analytics and business intelligence research: A systematic review

2020 ◽  
Vol 57 (6) ◽  
pp. 102279
Author(s):  
Jaewoong Choi ◽  
Janghyeok Yoon ◽  
Jaemin Chung ◽  
Byoung-Youl Coh ◽  
Jae-Min Lee
Keyword(s):  
Systematic Review ◽  
Social Media ◽  
Business Intelligence ◽  
Social Media Analytics

Social Media Business Intelligence

2013 ◽  
Vol 5 (3) ◽  
pp. 51-73 ◽  
Author(s):  
David Bell ◽  
Sara Robaty Shirzad
Keyword(s):  
Social Media ◽  
Business Intelligence ◽  
Global Business ◽  
Market Participants ◽  
Domain Specific ◽  
Business Environments ◽  
Social Media Tools ◽  
Relationships Management ◽  
Business To Business ◽  
Social Media Network

Social media tools are increasingly used for relationships management among marketplace actors (e.g. organisations, suppliers and individuals). As markets become ever more global and dynamic, new entrants find themselves struggling to fully understand the marketplace, companies operating with it and changes that occur. The authors discuss Social Media Network (SMN) tools and outline a methodology and procedure that supports the identification of domain specific networks within particular global business-to-business environments. Research is carried out using SMN data about firms in the pharmaceutical industry. The authors use their own methodology to uncover market participants, linkages and prominent issues that may help new firms to position themselves effectively within a new marketplace. SMNs provide a sizable source of information and new approaches are required to fully leverage their considerable value. This paper explores how SMNs can be used as an effective source of business intelligence by utilising two popular SMN platforms.

Interaction Mining

2012 ◽  
pp. 688-709
Author(s):  
Vincenzo Pallotta ◽  
Lammert Vrieling ◽  
Rodolfo Delmonte
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Future Development ◽  
Business Intelligence ◽  
Relationship Management ◽  
Marketing Research ◽  
Customer Relationship ◽  
Unstructured Data ◽  
Social Media Analytics ◽  
Business Analytics

In this chapter we present the major challenges of a new trend in business analytics, namely Interaction Mining. With the proliferation of unstructured data as the result of people interacting with each other using digital networked devices, classical methods in text business analytics are no longer effective. We identified the causes of their failure as being related to the inadequacy of dealing with conversational data. We propose then to move from Text Mining towards Interaction Mining, and we make several cases for this transition in areas such as marketing research, social media analytics, and customer relationship management. We also propose a roadmap for the future development of Interaction Mining by challenging the current practices in business intelligence and information visualization.

Interaction Mining

2011 ◽  
pp. 122-146 ◽  
Author(s):  
Vincenzo Pallotta ◽  
Lammert Vrieling ◽  
Rodolfo Delmonte
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Future Development ◽  
Business Intelligence ◽  
Relationship Management ◽  
Marketing Research ◽  
Customer Relationship ◽  
Unstructured Data ◽  
Social Media Analytics ◽  
Business Analytics

In this chapter we present the major challenges of a new trend in business analytics, namely Interaction Mining. With the proliferation of unstructured data as the result of people interacting with each other using digital networked devices, classical methods in text business analytics are no longer effective. We identified the causes of their failure as being related to the inadequacy of dealing with conversational data. We propose then to move from Text Mining towards Interaction Mining, and we make several cases for this transition in areas such as marketing research, social media analytics, and customer relationship management. We also propose a roadmap for the future development of Interaction Mining by challenging the current practices in business intelligence and information visualization.

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