Amazing technological breakthrough possible @S-Logix pro@slogix.in

Office Address

  • #5, First Floor, 4th Street Dr. Subbarayan Nagar Kodambakkam, Chennai-600 024 Landmark : Samiyar Madam
  • pro@slogix.in
  • +91- 81240 01111

Social List

Machine learning-based mathematical modelling for prediction of social media consumer behavior using big data analytics - 2021

Machine Learning-Based Mathematical Modelling For Prediction Of Social Media Consumer Behavior Using Big Data Analytics

Research Area:  Machine Learning

Abstract:

Social media is popular in our society right now. People are using social media platforms to purchase various products. We collected the data from various social media platforms. We analyzed the data for prediction of the consumer behavior on the social media platform. We considered the consumer data from Facebook, Twitter, Linked In and YouTube, Instagram, and Pinterest, etc. There are diverse and high-speed, high volume data which are coming from social media platform, so we used predictive big data analytics. In this paper, we have used the concept of big data technology to process data and analyze it to predict consumer behavior on social media. We have analyzed consumer behavior on social media platforms based on some parameters and criteria. We analyzed the consumer perception, attitude towards the social media platform. To get good quality of result, we pre-process data using various data pre-processing to detect outlier, noises, error, and duplicate record. We developed mathematical modeling using machine learning to predict consumer behavior on the social media platform. This model is a predictive model for predicting consumer behavior on the social media platform. 80% of data are used for training purposes and 20% for testing.

Keywords:  

Author(s) Name:  Kiran Chaudhary, Mansaf Alam, Mabrook S. Al-Rakhami & Abdu Gumaei

Journal name:  Journal of Big Data

Conferrence name:  

Publisher name:  Springer

DOI:  https://doi.org/10.1186/s40537-021-00466-2

Volume Information:  volume 8, Article number: 73