Análise da correlação entre estilo textual e opiniões em redes sociais multiculturais por meio de técnicas de processamento de linguagem natural

Abstract

This study investigates the relationship between writing style and expressed opinions in mul-ticultural social media by using embeddings generated with the STAR model (style) and the Qwen3-Embedding model (opinion). Although the linear correlation between both dimensions is nearly zero (r ≈ 0.01), the bivariate analysis revealed a highly structured elliptical distribution, consistent with the geometry of an anisotropic bivariate normal distribution. This pattern indi-cates the absence of linear dependency but the presence of a modelable nonlinear dependency, expressed through the anisotropy and unequal variance along the latent dimensions. The high-density region concentrated around intermediate similarity values suggests that most authors share median stylistic patterns, while opinions spread more broadly and multimodally. The findings demonstrate that style and opinion are not independent; rather, they are jointly organized within a structured, non-monotonic latent space typical of Transformer-based representations. Limitations related to data heterogeneity and the use of pretrained embeddings are discussed, and future research directions involving nonlinear methods, such as multivariate Gaussian regression, Kernel CCA, and neural embedding-based clustering are proposed.