Fuzzy Logic-Based Unsupervised Sentiment Analysis and Opinion Mining: Applications in Market Research

Analyzing user sentiments in marketing and enhancing customer experiences are essential for developing effective marketing strategies. This analysis is crucial for assessing the performance of social media platforms as communication tools. This research was practical in nature and cross-sectional in time, while utilizing both quantitative and qualitative variables within a descriptive research design. The study categorized user tweets without relying on prior knowledge or labeled data, employing fuzzy systems and an unsupervised approach. This advancement in sentiment analysis enabled researchers and practitioners to extract valuable insights from user opinions and emotions within their respective domains and platforms, thereby facilitating informed business decisions aimed at maximizing profitability. As a case study, this empirical research examined user experiences with Samsung and Apple mobile phones from 2022 to the present, classifying sentiments into positive, negative, and neutral categories. Three sentiment analysis tools—SentiWordNet, AFINN, and VADER—were employed to determine the polarity of the tweets. The classification results revealed a higher level of user satisfaction with Samsung mobile phones compared to Apple.