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Topic Identification and Prediction Using Sanskrit Hysynset

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Pervasive Computing and Social Networking

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 475))

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Abstract

The topic model implementation is not a new concept for English corpus due to the availability of plenty of resources, but developing a topic model for Sanskrit is comparatively an untouched area. The proposed approach is a 4 phased. The first phase constructs Hysynset followed by building a topic model that acts as a second phase. In the third phase, clustering is applied and the approach completes with classification and prediction that is the fourth phase. Hypernyms-hyponyms and synonyms are grouped in the first phase to reduce the dimensions and creates semantic space. The topic model is built using Latent Dirichlet Allocation (LDA) which shows very specific and informative topics as it uses Hysynset vector space model for Sanskrit (HSVSMS). The dataset belongs to more than 1100 Sanskrit stories. The documents’ wise topics are presented using dendrogram obtained after applying HAC and then supervised model that is random forest is used to predict the topic of the test/new document and evaluated using classification error and accuracy. In the absence of the availability of standard experiments, current work could not be compared with other existing work in case of prediction of stories. Comparative analysis of topic identification using existing technique Vector Space Model for Sanskrit (VSMS) proves that betterment of the proposed technique that is (HSVSMS) in the form of the accuracy, misclassification error of classification, coherence score, entropy and purity and topic titles.

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Authors and Affiliations

  1. Symbiosis Institute of Computer Studies and Research, Symbiosis International Deemed University, Pune, India

    Prafulla B. Bafna & Jatinderkumar R. Saini

Authors
  1. Prafulla B. Bafna
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  2. Jatinderkumar R. Saini
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Correspondence to Prafulla B. Bafna .

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Editors and Affiliations

  1. Electronics and Communication Engineering, Gnanamani College of Technology, Namakkal, India

    G. Ranganathan

  2. Czech Technical University in Prague, Prague, Czech Republic

    Robert Bestak

  3. Ryerson Communications Lab, Toronto, ON, Canada

    Xavier Fernando

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Bafna, P.B., Saini, J.R. (2023). Topic Identification and Prediction Using Sanskrit Hysynset. In: Ranganathan, G., Bestak, R., Fernando, X. (eds) Pervasive Computing and Social Networking. Lecture Notes in Networks and Systems, vol 475. Springer, Singapore. https://doi.org/10.1007/978-981-19-2840-6_14

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  • DOI: https://doi.org/10.1007/978-981-19-2840-6_14

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-2839-0

  • Online ISBN: 978-981-19-2840-6

  • eBook Packages: EngineeringEngineering (R0)

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