A Study on the Correlation Between User Sentiments and QoE Parameters in Video Streaming Services

Abstract

Social media has become a vital platform for user interaction, offering valuable insights into service quality for the video streaming industry. As competition intensifies, understanding user satisfaction through Quality of Experience (QoE) is critical for retaining users and improving services. Traditional QoE measurement methods are often costly and time-consuming, making efficient alternatives necessary. This study investigates the correlation between user sentiments on social media and QoE parameters (human, content, context, and system) while exploring sentiment analysis as a reliable tool for service improvement. A quantitative correlational design was employed, analyzing data from the official social media accounts of the top four global video streaming platforms and one leading service in Indonesia. After that, text mining techniques such as tokenization, removal of stop-words, and stemming were applied. However, they were labeled and classified into QoE parameters to prevent invalid user comments. The study employed correlation analysis to examine the connection between user sentiments and QoE parameters in video streaming services. A total of 2,556 data points were chosen for a complete analysis of the 11,280 data points obtained. Specifically, the analysis revealed a strong positive correlation of 98.16% between user sentiments and QoE parameters. The research results confirm that social media sentiment analysis is an efficient and accurate alternative to traditional QoE measurement approaches. This approach offers a data-based, practical evaluation of user satisfaction and QoE based on the analysis. The analysis concludes with practical recommendations for optimizing service delivery based on user perception in the video streaming industry.