A New Way of Topic Modeling Using MALLET for Current Job Trends

Abstract: Topic modeling is the process of extracting topics from texts. A topic can be viewed as a collection or cluster of words that occur together and frequently. Latent Dirichlet Allocation(LDA), a statistical topic model is used to extract topics from the collected corpus which is a collection of job related data from LinkedIn. LDA is an unsupervised machine learning approach. We analyzed the interrelationship between topics and represented it graphically. The recent job trends in the industry can be interpreted easily using this representation. Keywords: Topics, Topic Modeling, MALLET, Latent Dirichlet Allocation(LDA), Gephi.