Consumer Behaviour Analysis In Social Network Using Big Data

Document Type : Primary Research paper


1 Assistant Professor, Department of Computer Technology-PG, Kongu Engineering College,Perundurai

2 Assistant Professor, Department of Computer Science, Government Arts College,Karur

3 Student, Department of Computer Technology-PG, Kongu Engineering College,Perundurai


In present, the social network plays a vital role in every person of the society
irrespective of age, community, gender, business background etc… At present, 3.4 billion
to 3.6 billion people were connected through social networks, and also there will be an
increase in the number of around 4.50 billion by 2025. This massive usage of social media
platforms paves the way to many E-Commerce companies to promote their growth by
targeting the customers according to their needs. But it is not easy for the companies to
reach their respective customers as they want to analyze huge data that were generated in a
social network of different age groups and backgrounds. To analyze these huge volumes of
data, Big Data comes into the role which handles both the structured and unstructured
data. An algorithm is needed to implement on these data that analyses and classifies the
consumer based on their behavior that satisfies their needs. To provide an optimal solution
to this problem two mechanisms are applied in this paper. One is applying Word2vec on
data that gives the numerical representation (vectors) of the words and the second is
applying K – Means Clustering on those numerical values which form the cluster of
similar words. By using those clusters companies can identify which product is more
needed or liked by the people. Therefore it is believed that this method provides a reliable
solution to businessenterprise.


Volume 12, Issue 3 - Serial Number 3
ICMMNT-2021 International Virtual Conference on Materials, Manufacturing and Nanotechnology, 30th June, 2021.
June 2021
Pages 1367-1374