Enhancing fan base engagement through explainable self-learning sentiment analysis

Abstract

Individuals and brands with large fan bases face difficulty in understanding fan sentiment and its potential impact on fan engagement and performance. This is particularly pertinent within fast-paced sports such as Formula 1, where fan opinions can significantly influence driver and team morale. To address the above-mentioned problem, this study proposes the use of deep learning-based sentiment analysis techniques to enhance fan base engagement. The system would act as a tool to automate the process of marketing and public relations teams by analysing textual data provided by the fan base and providing meaningful insight/reports of the fan base’s emotion of the posted content.This is achieved by a novel multi-class sentiment analysis system that utilizes a fine-tuned Distil-BERT model for classification, self-supervised techniques for self-improvement, and an explainable AI (XAI) approach for interpretability. The proposed system demonstrated strong performance during testing and evaluation, achieving an overall accuracy of 82% and an F1-score of 80%. Overall, the systems components and structure focuses on utilizing the least amount of resources while also maintaining a high prediction accuracy and speed. This ultimately results in a budget friendly and robust tool that can be integrated into bigger analytics systems.