Decision making using machine learning based opinion prediction model for smart governance

被引:0
作者
Kumar A. [1 ]
Sharma A. [1 ]
机构
[1] Department of Computer Science & Engineering, Delhi Technological University, Delhi
关键词
Big data; Decision making; Opinion mining; Policy evaluation; Scheme; Twitter;
D O I
10.2174/2213275912666191026123414
中图分类号
学科分类号
摘要
Background: Decision making requires a rigorous process of evaluation, which is an ana-lytical and organized process to figure out the present positive influences, favourable future pro-spects, existing shortcomings and ulterior complexities of any plan, program, practice or a polity. Evaluation of policy is an essential and vital process required to measure the performance or pro-gression of the scheme. The main purpose of policy evaluation is to empower various stakeholders and enhance their socio-economic environment. A large number of policies or schemes in different areas are launched/constituted by government in view of the citizen welfare. Although, the governmental policies intend to better shape up the quality of life of people but may also impact their eve-ryday's life. Objective: The contemplation of public opinion plays a very significant role in the process of policy evaluation. Therefore, the aim of this paper is to incorporate the concept of opinion mining in policy evaluation. An attempt has been made to elevate the process of policy evaluation by analyzing public opinion. Methods: A latest governmental scheme Saubhagya launched by the Indian government in 2017 has been selected for evaluation by applying supervised learning based opinion mining techniques. The data set of public opinion associated with this scheme has been captured by Twitter. Results: The result validates that the proposed methodology supports the optimizing process of policy evaluation and provides a more accurate and actual status of policy's effect among Indian citizen. As a result, this would aid in identifying and implementing the preventive and corrective measures required to be taken for a successful policy. Conclusion: The proposed methodology will stabilize and strengthen the process of policy evaluation which target towards favourable and flourishing future prospects concerning the socio-economic status of a nation. The results are quite exciting and further extension of work will be performed in order to develop and design a patent framework in the area of social big data analytics. © 2021 Bentham Science Publishers.
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页码:1402 / 1411
页数:9
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  • [1] Oxford Dictionaries
  • [2] Bhatia M. P. S., Khalid A. K., A primer on the web information retrieval paradigm, J. Theor. Appl. Inform. Tech, 4, 7, (2008)
  • [3] Kumar A., Sharma A., Socio-Sentic framework for sustaina-ble agricultural governance, Sustain. Comput-Inform. Syst, (2018)
  • [4] Bhatia M. P. S., Kumar A., Paradigm shifts: From pre-web information systems to recent web-based contextual information re-trieval, Webology, 7, (2010)
  • [5] Mikroyannidis A., Toward a social semantic web, Computer, 40, 11, pp. 113-115, (2007)
  • [6] Dinger M., Grover V., Revisiting Web 2.0, Advanced Methodologies and Technologies in Network Architecture, Mobile Computing, and Data Analytics, pp. 1777-1788, (2019)
  • [7] Saubhagya scheme: All you need to know
  • [8] Kumar A., Sebastian T. M., Sentiment analysis on twitter, Int. J. Comput. Sci, 9, 4, (2012)
  • [9] Singh R., Goel V., Various machine learning algorithms for twitter sentiment analysis, Information and Communication Technology for Competitive Strategies, pp. 763-772, (2019)
  • [10] Policy