Psychiatry texts provide critical insights into patient mental states and therapeutic interactions. These texts are essential for understanding psychiatric conditions, treatment dynamics, and patient responses. However, the complex and diverse nature of psychiatric communications poses significant challenges for traditional topic modeling methods. The intricate language, subtle psychological nuances, and varying lengths of text segments make it difficult to extract coherent and meaningful topics. Conventional approaches often struggle to capture the depth and overlap of themes present in these texts. In this study, we present a novel approach to topic modeling that addresses these limitations by reformulating the problem as a community detection task within a graph constructed from the text corpus. Our methodology includes lemmatization for data standardization, TF-IDF vectorization to create a term-document matrix, and cosine similarity computation to produce a similarity matrix. This matrix is then binarized to form a graph, on which community detection is performed using the Louvain method. The detected communities are subsequently analyzed with Latent Dirichlet Allocation (LDA) to extract topics. Our approach outperforms traditional topic modeling methods, offering more accurate and interpretable topic extraction with improved coherence and lower perplexity.