Topic Extraction of Crawled Documents Collection using Correlated Topic Model in MapReduce Framework
This addresses scalability issues for researchers analyzing large document sets, but it is incremental as it adapts an existing model to a distributed framework.
The paper tackled the scalability problem in topic modeling for large document collections by implementing the Correlated Topic Model with variational EM in MapReduce, achieving comparable topic coherence to MapReduce LDA.
The tremendous increase in the amount of available research documents impels researchers to propose topic models to extract the latent semantic themes of a documents collection. However, how to extract the hidden topics of the documents collection has become a crucial task for many topic model applications. Moreover, conventional topic modeling approaches suffer from the scalability problem when the size of documents collection increases. In this paper, the Correlated Topic Model with variational Expectation-Maximization algorithm is implemented in MapReduce framework to solve the scalability problem. The proposed approach utilizes the dataset crawled from the public digital library. In addition, the full-texts of the crawled documents are analysed to enhance the accuracy of MapReduce CTM. The experiments are conducted to demonstrate the performance of the proposed algorithm. From the evaluation, the proposed approach has a comparable performance in terms of topic coherences with LDA implemented in MapReduce framework.