Enhancing Topic Prediction Using Machine Learning Techniques and ConceptNet-based Cosine Similarity Measure

Abstract

Topic modelling is one of the important research area of Natural Language Processing. Researchers have tried topic modelling for classification, categorization etc., and even the methods of topic modelling are used for sentiment analysis and other areas of language processing. In this paper, a novel method based on cosine similarity is used for topic modelling using Machine Learning Techniques. Cosine similarity measure on synonyms of topics present in the BBC News and Yahoo datasets from Concept Net is a pioneering approach. It has been observed that machine learning techniques applied here enhance accuracy compared to previously applied methods on these two datasets. On Yahoo datasets, the proposed technique increases the accuracy by 8% even over deep learning methods, which shows the efficiency and applicability of the research. Uses of ConceptNet is a novel idea over here, and the combination of ConceptNet with machine learning made the effort successful in comparison to existing state-of-the-art research on topic modelling.