Abstract
This study explores the intersection of fashion trends and social media sentiment through computational analysis of Twitter data using the T4SA (Twitter for Sentiment Analysis) dataset. By applying natural language processing and machine learning techniques, we examine how sentiment patterns in fashion-related social media conversations can serve as predictors for emerging fashion trends. Our analysis involves the identification and categorization of fashion-related content, sentiment classification with improved normalization techniques, time series decomposition, statistically validated causal relationship modeling, cross-platform sentiment comparison, and brand-specific sentiment analysis. Results indicate correlations between sentiment patterns and fashion theme popularity, with accessories and streetwear themes showing statistically significant rising trends. The Granger causality analysis establishes sustainability and streetwear as primary trend drivers, showing bidirectional relationships with several other themes. The findings demonstrate that social media sentiment analysis can serve as an effective early indicator of fashion trend trajectories when proper statistical validation is applied. Our improved predictive model achieved 78.35% balanced accuracy in sentiment classification, establishing a reliable foundation for trend prediction across positive, neutral, and negative sentiment categories.
Abstract (translated)
这项研究通过计算分析Twitter数据（使用T4SA（Twitter for Sentiment Analysis）数据集），探索了时尚趋势与社交媒体情绪之间的交集。通过应用自然语言处理和机器学习技术，我们探讨了与时尚相关的社交媒体对话中的情感模式如何作为新兴时尚趋势的预测指标。 我们的分析包括识别和分类与时尚相关的内容、改进的情感归一化技术下的情感分类、时间序列分解、统计验证因果关系建模、跨平台情绪比较以及品牌特定的情绪分析。结果表明，情感模式与时尚主题受欢迎程度之间存在关联，配饰和街头服饰主题显示出统计上显著的增长趋势。 格兰杰因果分析建立了可持续性和街头服饰作为主要的趋势驱动因素，并展示了与其他多个主题之间的双向关系。研究发现证明了当应用适当的统计验证时，社交媒体情绪分析可以作为一种有效的早期指标来预测时尚趋势的发展轨迹。 我们改进的预测模型在情感分类中实现了78.35%的平衡准确性，在正面、中性和负面情绪类别上建立了可靠的趋势预测基础。
URL
https://arxiv.org/abs/2505.00050
