Social Media Opinion Leader Identification Based on Sentiment Analysis

Abstract

Growing number of enterprises nowadays are pursuing online marketing strategies, with their eyes focusing on the effectiveness of opinion leader value-creation on social media platform. Therefore, how to accurately identify opinion leaders on social media platforms is of great significance. The emotional value generated by communication between opinion leaders and fans will have a significant impact on the potential consumption behavior of fans. Most of the existing research on opinion leader identification is to establish models based on the existing indicator data of the platform, without taking the value of emotional communication into account. This paper proposes a social media opinion leader identification model based on online comment sentiment analysis. We first crawl online comments, then analyze the text data characteristics, establish emotional indicators of different attributes, calculate the sentiment value of the text data, and finally use artificial neural network technology to train to form an opinion leader recognition model. The experimental results show that emotional communication is a very important factor in opinion leader identification, and the proposed model can identify opinion leaders more accurately.