Abstract
Topic models allude to statistical algorithms for finding out an extensive text body’s latent semantic structures. Standing here in today’s world, the measure of the textual data and information we come across in our day-to-day lives is basically beyond our handling limit. Topic models can provide a way out for us to understand and manage the vast accumulations of unstructured textual data and information. Initially emerged as a text-mining instrument, topic models have found applications in various other fields. This paper makes a thorough comparative study of LSA with that of commonly used TF-IDF approach for text classification and proves that LSA yields better accuracy in classifying texts. The novelty of the paper lies in the fact that we are using a much sparser representation than usual TF-IDF and also, LSA can get from the topic if there are any synonym words. This paper proposes a method, using the concept of entropy, which further increases the accuracy of text classification.
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Acknowledgements
We gratefully acknowledge the contribution of MUST Research Club and CU Data Science Group. MUST Research Club is a non-profit organization registered under Society Act of India. MUST Research Club is dedicated to promote excellence and competence in the field of data science, cognitive computing, artificial intelligence, machine learning and advanced analytics for the benefit of the society. Calcutta University Data Science Group is a group to create solutions to solve societal problems, as well as form generic solutions to common data science and engineering issue. This is a forum which brings together researchers, industry practitioners, scholars, interns and form groups.
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Neogi, P.P.G., Das, A.K., Goswami, S., Mustafi, J. (2020). Topic Modeling for Text Classification. In: Mandal, J., Bhattacharya, D. (eds) Emerging Technology in Modelling and Graphics. Advances in Intelligent Systems and Computing, vol 937. Springer, Singapore. https://doi.org/10.1007/978-981-13-7403-6_36
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DOI: https://doi.org/10.1007/978-981-13-7403-6_36
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