Topic Modeling For Analyzing Language Patterns In Online Texts

Abstract

The rapid growth of digital communication has led to the generation of vast amounts of textual data in online platforms, ranging from social media to academic forums. Understanding language patterns within these texts is essential for insights into user behavior, sentiment, and communication trends. This study applies topic modeling techniques, particularly Latent Dirichlet Allocation (LDA), to analyze language patterns in online texts. The approach enables the identification of dominant topics, trends, and semantic relationships among words in large text corpora. Data were collected from multiple online platforms, preprocessed for cleaning and normalization, and analyzed using Python-based topic modeling tools. The results demonstrate the effectiveness of topic modeling in revealing underlying themes and patterns, providing valuable insights for researchers, educators, and platform administrators to better understand digital communication behaviors. This research contributes to the field of computational linguistics by offering a scalable methodology for automated analysis of large-scale online textual data.