Improving Fashion Sentiment Detection on X through Hybrid Transformers and RNNs

X (formerly known as Twitter) is one of the most prominent social media platforms, enabling users to share short messages (tweets) with the public or their followers. It serves various purposes, from real-time news dissemination and political discourse to trend spotting and consumer engagement. X has emerged as a key space for understanding shifting brand perceptions, consumer preferences, and product-related sentiment in the fashion industry. However, the platform’s informal, dynamic, and context-dependent language poses substantial challenges for sentiment analysis, mainly when attempting to detect sarcasm, slang, and nuanced emotional tones. This study introduces a hybrid deep learning framework that integrates Transformer encoders, recurrent neural networks (i.e., Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)), and attention mechanisms to improve the accuracy of fashion-related sentiment classification. These methods were selected due to their proven strength in capturing both contextual dependencies and sequential structures, which are essential for interpreting short-form text. Our model was evaluated on a dataset of 20,000 fashion tweets. The experimental results demonstrate a classification accuracy of 92.25%, outperforming conventional models such as Logistic Regression, Linear Support Vector Machine (SVM), and even standalone LSTM by a margin of up to 8%. This improvement highlights the importance of hybrid architectures in handling noisy, informal social media data. This study’s findings offer strong implications for digital marketing and brand management, where timely sentiment detection is critical. Despite the promising results, challenges remain regarding the precise identification of negative sentiments, indicating that further work is needed to detect subtle and contextually embedded expressions.