DocumentCode :
3082013
Title :
Topical Evolution and Regional Affinity of Tweets
Author :
Dey, Lipika ; Khurdiya, Arpit ; Mahajan, Dhruv
Author_Institution :
Innovation Labs., Delhi Tata Consultancy Services, Gurgaon, India
fYear :
2013
fDate :
24-26 Aug. 2013
Firstpage :
297
Lastpage :
300
Abstract :
Business organizations are increasingly showing interest in Twitter content to know their consumers. Tracking popular tags and trends give some idea about what people are talking about. However, in order to act on the knowledge acquired, they need more detailed information like regional variability in content, exact location of discontent if any, regional affinities and influences etc. In this work, we present methods to identify topics of discussion in tweets using a LDA-based approach, which can identify emerging or evolving topics. Regional analysis of topics can provide interesting business insights about consumer expectation or behavioural variations. Further, regional distribution of topics are analysed to identify clusters of regions that tend to behave similarly over extended periods of time.
Keywords :
business data processing; consumer behaviour; data mining; social networking (online); statistical analysis; text analysis; LDA-based approach; Twitter content; behavioural variation; business insight; business organization; consumer expectation; popular tags; regional affinity; regional variability; topic regional distribution; topical evolution; trends; tweets; Data mining; Market research; Matrix decomposition; Media; Organizational aspects; Twitter; Clustering; Regional Dispersion; Social Media Analytics; Topic extraction; regional clusters;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational and Business Intelligence (ISCBI), 2013 International Symposium on
Conference_Location :
New Delhi
Print_ISBN :
978-0-7695-5066-4
Type :
conf
DOI :
10.1109/ISCBI.2013.66
Filename :
6724371
Link To Document :
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