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Data Management, Analytics and Innovation pp 197–208Cite as

Hybrid Kmeans with Improved Bagging for Semantic Analysis of Tweets on Social Causes

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 808))

Abstract

Analysis of public information from social media could yield fascinating outcomes and bits of knowledge into the universe of general conclusions about any item, administration, or identity. Social network data is one of the most effective and accurate indicators of public sentiment. Analysis of the mood of public on a particular social issue can be easily judged by several methods developed by the technicians. In this paper, analysis of the mood of society towards any particular news from the twitter post in form of tweets. The key objective behind this research is to increase the accuracy and effectiveness of the classification by the process of the NLP that is Natural Language Processing Techniques while focusing on semantics and World Sense Disambiguation. The process of classification includes the combination of the effect of various independent classifiers on one particular classification problem. The data that is available in the form of tweets on twitter can easily frame the insight of the public attitude towards the particular tweet. The proposed work is well planned to design as well as implement the best hybrid method that includes Hybrid Kmeans/Modified Kmeans (MKmeans) that involves clustering and Bagging for sentiment analysis. With this proposed idea one can easily understand the behavior of the public towards the post and further assist in the future policy making taking the results as the basis. At the end results are compared with the existing model with the motive of validating the findings.

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Author information

Authors and Affiliations

  1. IBM India Pvt. Ltd., Bengaluru, Karnataka, India

    Mani Madhukar

  2. Banasthali Vidyapeeth, Banasthali, Rajasthan, India

    Seema Verma

Authors
  1. Mani Madhukar
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  2. Seema Verma
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Corresponding author

Correspondence to Mani Madhukar .

Editor information

Editors and Affiliations

  1. Department of Automatics and Applied Software, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  2. Audyogik Tantra Shikshan Sanstha’s, IICMR, Pune, Maharashtra, India

    Neha Sharma

  3. Faculty of Engineering and Technology, A. K. Choudhury School of Information Technology, Kolkata, West Bengal, India

    Amlan Chakrabarti

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© 2019 Springer Nature Singapore Pte Ltd.

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Cite this paper

Madhukar, M., Verma, S. (2019). Hybrid Kmeans with Improved Bagging for Semantic Analysis of Tweets on Social Causes. In: Balas, V., Sharma, N., Chakrabarti, A. (eds) Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing, vol 808. Springer, Singapore. https://doi.org/10.1007/978-981-13-1402-5_15

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  • DOI: https://doi.org/10.1007/978-981-13-1402-5_15

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1401-8

  • Online ISBN: 978-981-13-1402-5

  • eBook Packages: EngineeringEngineering (R0)

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