An innovative method for topic modeling employing speed reading

As the pace of product reviews and similar textual content accelerates, the power of natural language becomes evident in its ability to effectively convey vast and intricate information through succinct communication. This proficiency is applied in Latent Dirichlet Allocation (LDA), a machine-learning algorithm that explores documents to uncover hidden topics. This principle, LDA, proves crucial in generating summaries from a provided collection of documents, significantly expediting and enhancing the comprehension of textual data. Yet, LDA faces difficulties in accurately identifying topics within concise documents or within data that is noisy. This article introduces an innovative strategy for topic modeling using LDA, centered around speed reading’s influence on sentences. From the experimental results, it was found that our suggested approach is more powerful than the baseline method in terms of coherence rate (75.64%), and execution time (1.395 seconds). The overall approach, evaluated via evaluating metrics, demonstrates that speed reading using LDA is efficacy in identifying text data attributes.