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Prioritizing Public Grievance Redressal Using Text Mining and Sentimental Analysis

  • Rama Krushna DasEmail author
  • Manisha Panda
  • Sweta Shree Dash
Conference paper
  • 32 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1082)

Abstract

After successful implementation of online grievance monitoring systems by different government agencies, the grievance submission by common citizen has increased many folds. As the number exponentially increases, it becomes difficult for the government authorities to redress the grievances timely, efficiently, and effectively. In this paper, the authors are proposing different text mining and sentimental analysis techniques, on the content of the grievance, to prioritize the grievances submitted to the Chief Minister (CM) grievance cell, Odisha Province. Using these techniques, the grievances are prioritized as high priority, medium priority, and low priority. It helps the concerned government authorities to redress the top priority grievances within a stipulated time period, in comparison to medium and low priority grievances. This helps the needy and common citizen to get timely public services and government support, and their faith and confidence increases on the government machinery.

Keywords

Text mining Sentiment analysis Lexicon Afinn Bing Grievance Grievance redressal High priority Medium priority Low priority 

References

  1. 1.
    Kumar, S.B., Karthika R.: A survey on text mining process and techniques. Int. J. Adv. Res. Comput. Eng. Technol. (IJARCET) 3(7) (2014)Google Scholar
  2. 2.
    Mustafaraj, E., Hoof, M., Freisleben, B.: Mining diagnostic text reports by learning to annotate knowledge roles. In: Natural Language Processing and Text Mining. ISBN-10: 1-84628-175-X, ISBN-13: 978-1-84628-175-4Google Scholar
  3. 3.
    Talib, R., Hanif, M.K., Ayesha, S., Fatima, F.: Text mining: techniques, applications and issues. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 7(11) (2016)Google Scholar
  4. 4.
    Inokuchi, A., Takeda, K.: A method for online analytical processing of text data. In: CIKM’07, 6–8 Nov 2007, Lisboa, Portugal, Copyright 2007 ACM 978-1-59593-803-9/07/0011Google Scholar
  5. 5.
    Jin, W., Ho, H.H., Srihari, R.K.: OpinionMiner: a novel machine learning system for web opinion mining and extraction. In: KDD’09, June 28–July 1, 2009, Paris, France, Copyright 2009 ACM 978-1-60558-495-9/09/06Google Scholar
  6. 6.
    Yogapreethi, N., Maheswari, S.: A review on text mining in data mining. Int. J. Soft Comput. (IJSC). 7(2/3) (2016)Google Scholar
  7. 7.
    Fatima, E.B., Abdelmajid, E.M.: A new approach to text classification based on naïve Bayes and modified TF-IDF algorithms. In: SCAMS 17, 25–27 Oct 2017, Tangier, Morocco, ACM 978-1-4503-5211-6/17/10Google Scholar
  8. 8.
    Jusoh, S., Alfawareh, H.M.: Techniques, applications and challenging issue in text mining. IJCSI Int. J. Comput. Sci. 9(6), no. 2 (2012). ISSN (Online) 1694-0814Google Scholar
  9. 9.
    Yuan, C., Yue, Y., Wei, S., Yin, N.: A quality evaluation model for android system based on forum text mining. In: IEEE International Conference on Knowledge Engineering and Applications (2016)Google Scholar
  10. 10.
  11. 11.
    https://pgportal.gov.in/. Accessed 18 June 2018
  12. 12.
  13. 13.
    https://www.rstudio.com/. Accessed 18 June 2018
  14. 14.

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Rama Krushna Das
    • 1
    Email author
  • Manisha Panda
    • 2
  • Sweta Shree Dash
    • 3
  1. 1.National Informatics CentreBerhampurIndia
  2. 2.Berhampur UniversityBerhampurIndia
  3. 3.Institute of Technical Education and ResearchBhubaneswarIndia

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