Improved topic modeling with parallel-supervised LDA

Abstract

In the modern era of digitalization, our day-to-day life is entirely dependent on digital platform-from raising our voice in social media to online shopping. Our collective knowledge is continued to be accumulated in the form of electronic texts, blogs, news, images, audios, videos and in many more ways and on account of this there is a greater need of analyzing these huge contents to get rid of the difficulties in searching the object, we aim for. Topic modelling is an efficient machine learning techniques for discovering the hidden semantic structure of contents. “Latent Dirichlet Allocation” (LDA) is a generative probabilistic topic modelling, which is the basis of other generative topic modelling techniques. New models are coming up with advanced algorithms in order to improve the topic modelling. Existing models have their own limitation. In case of obtaining more accuracy, the processing time of topic modelling goes high while in consideration of achieving more speed, accuracy gets low. Most of the algorithms implemented earlier cannot perform well in above-mentioned area. In this paper, we would like to introduce parallel-supervised LDA model where supervised Latent Dirichlet Algorithm (sLDA) and parallel Latent Dirichlet Algorithm (pLDA) are applied together to obtain high accurate results with quicker response time.