Effective Comparison of LDA with LSA for Topic Modelling

The process of converting unstructured data into a structured readable format is becoming hard day by day. Till day every organization consists of more than 80% of its operational data in an unreadable format. The proposed method helps in converting unreadable data to a readable structured format with the help of Machine learning were classification, and clustering plays a crucial role in converting the operational data into data models and visualize the processed information to the end-user. As organizations have specific requirements, considering them, we are going to implement latent dirichlet allocation (LDA) and latent semantic analysis (LSA), which were able to handle discrete data. Also, a comparison is made to test divergence, throughput, quality, and response time, as both of them can classify the data based on the content and by giving labels to each category. The algorithm with better divergence is implemented that can handle the organizational requirements by presenting the top areas that need to improve/concentrate depending on the analytics made by the algorithm on the available discrete data, and by implementing visualization techniques, the results will be even displayed in graphical format.