Document clustering is a fundamental task in text mining and information retrieval, which aims to organize a collection of documents into meaningful groups based on their content and structure. However, traditional clustering approaches often fail to capture the complex and nuanced relationships between documents, particularly in the context of corporate social responsibility news analysis. To address this challenge, this paper explores the potential of multi-view fuzzy clustering techniques to optimize document clustering and uncover valuable insights from 9000 CSR news articles. The proposed method integrates multiple clustering models that use BERT Encoders as text representation models, drawing on the contextual and semantic information captured by these powerful language models. This approach leads to a final consensus clustering solution that leverages the complementary strengths of diverse text representations, thereby improving the ability to uncover hidden patterns and relationships within the text data. The findings show that the proposed multi-view fuzzy clustering framework, incorporating SBERT and Fuzzy C-means, is an effective approach for uncovering the underlying topical structure and thematic relationships within the CSR news corpus, as evidenced by the higher Silhouette Coefficient and Calinski-Harabasz Index values, with a Calinski-Harabasz score of 13,998.20 and a Silhouette Coefficient score of 0.80, where higher scores indicate better clustering performance. The insights gained from this research can inform future efforts to leverage multi-view fuzzy clustering for enhanced understanding and analysis of textual data in various domains.