Soft Computing and Machine Intelligence

Analyzing the Performance of User Generated Contents in B2B Firms Based on Big Data and Machine Learning

Zeinab Shahbazi (a) and Yung-cheol Byun(a)*

(a) Department of Computer Engineering, Jeju National University, Jeju 63243, Korea
* Corresponding author ✉: ycb@jejunu.ac.kr

Abstract: Social media platforms act as a significant role in human life in recent decades. Marketing scholars show interest in the field of big data based on user-generated content from social media platforms. However, maximum user-generated content is conducted in terms of business-to-consumer (B2C) context to improve the knowledge differences in business to business (B2B) area. The dataset used in the proposed system collects from the Twitter platform. The extracted information is related to eight years of stock data related to 407 companies. Similarly, machine learning techniques are applied to predict data performance. The result of machine learning is converted to the monthly panel dataset. Based on the analysis results, user-generated contents have a considerable impact on companies, showing the differences between B2B and B2C firms. The generated results show that B2C performance is higher and more reliable than B2B. In this process, the consumer's positive response does not affect the stock data performance.