Mining Better Advertisement Tool for Government Schemes Using Machine Learning
DOI:
https://doi.org/10.61841/zvmn6683Keywords:
Government Schemes, Advertisement Tool, Classification, Class Imbalance, Mean Error Based Ensemble.Abstract
Public opinion impacts most government policies and sets the limits within which policymakers must operate. It also sets the pace of reform. Central and state governments allocate massive budget for advertising schemes. Advertisement is necessary for reaching out to maximum people. But more money is invested in the advertisement. This invested amount will be reduced if governments select proper advertisement medium (Printed/Television/Social). India is diverse behavior nation. Citizens of India have different kinds of diverse educational background, culture, etc., Habitation of people in rural, urban and tribal varies considerably. The same advertisement medium is not suitable to reach out to these diverse natured set of people. So this paper focuses on public opinion mining to find the best advertising medium for government schemes. Data set is collected through online. Collected citizen data include gender, qualification, government school/private school, the field of work, occupation, year of experience, city/village, age, government job/private job, type of advertising medium people are comfortable. The proposed system introduces two new classification algorithms to predict the best advertisement medium to advertise government schemes. The final results show that the performance of the proposed algorithms outperformed existing algorithms.
Downloads
References
[1] Amit Vasant Pandhare and Abrar Alvi, “Public Opinion Mining for Effective Policy Making”,
International Journal of Research in Advent Technology, E-ISSN: 2321-9637, Volume 3, No.4, April 2015.
[2] Andrea Di Maio, “Gartner Open Government Maturity Model”, Gartner Report, 2010.
[3] Ashna Bali, Payal Agarwal, Gayatri Poddar, Devyani Harsole, Nilufar M. Zaman, “Consumer’s Sentiment
Analysis of Popular Phone Brands and Operating System Preference”, International Journal of
Engineering and Technology (IJET), Volume 155, 2016.
[4] Bharat R.Naiknaware, Seema Kawathekar, Sachin N.Deshmukh, “Sentiment Analysis of Indian
Government Schemes Using Twitter Datasets”, IOSR Journal of Computer Engineering (IOSR-JCE), eISSN: 2278-0661,p-ISSN: 2278-8727, pp.70-78, 2016.
[5] Chip Gliedman, “Industry Innovation: US Federal Government”, Forrester Research Report, 2011.
[6] Chris Seiffert, Taghi M.Khoshgoftaar, Jason Van Hulse and Amri Napolitano, “RUSBoost: A Hybrid
Approach to Alleviating Class Imbalance”, IEEE Transactions on Systems and Cybernetics—Part A:
Systems And Humans, Volume 40, No. 1, 2010.
[7] D. Suganthi and A. Geetha, “Twitter Sentiment Analysis on GST tweets using R tool”, International
Journal of Scientific Research in Computer Science, Engineering and Information Technology, Volume 2,
Issue 5, ISSN : 2456-3307, 2017.
[8] Gustavo E.A.P.A.Batista, Ronaldo C.Prati and Maria Carolina Monard, “A Study of the Behaviour of
Several Methods for Balancing Machine Learning Training Data”, ACM SIGKDD Explorations, 2004.
[9] Hsinchun Chen and David Zimbra “AI and Opinion Mining”, Intelligent Systems IEEE, Volume 25, Issue
3, pp. 74 – 80, May-June 2010.
[10] J. Zabin, and A. Jefferies, “Social media monitoring and analysis: Generating consumer insights from
online conversation”, Aberdeen Group Benchmark Report, 2008.
[11] K.Nithya, M.Krishnamoorthi, M.Kalamani, “Tweets: Review of Micro-Blog Based Recommendation
Systems (RS) for News Recommendation (NR)”, International Journal of Recent Technology and
Engineering (IJRTE), ISSN: 2277-3878, Volume 7, Issue 4S, pp. 444-448, November 2018.
[12] K. Nithya, P.C.D. Kalaivaani, R. Thangarajan, “An enhanced data mining model for text classification”,
International Conference on Computing Communication and Applications (ICCCA) 2012, pp. 1-4, 22-24
February 2012.
[13] Nitesh V.Chawla, Nathalie Japkowicz and Aleksander Kolcz, “Editorial: Special Issue on Learning from
Imbalaned Data sets”, ACM Sigkdd Explorations, Volume 6, Issue 1 – pp. , 2004.
[14] N.V.Chawla, K.W.Bowyer, L.O.Hall and W.P.Kegelmyer, “SMOTE: Synthetic Minority Over-Sampling
Technnique Journal of the Artificial Intelligence Research”, Volume 16, pp.321–357, 2002.
[15] Omar Alqaryouti, Nur Siyam, Azza Abdel Monem, Khaled Shaalan, “Aspect-based sentiment analysis
using smart government review data”, Science Direct - Applied Computing and Informatics,
[16] Pengbo Zhang and Zhixin Yang, “A Robust AdaBoost. RT Based Ensemble Extreme Learning Machine”,
Mathematical Problems in Engineering, 2015.
[17] Prakash S, Vijayakumar M, Parvathi RMS,” A novel method of mining association rule with multilevel
concept hierarchy” Int. J. Comput. Appl. (IJCA), pp.26-29,2011.
[18] Prakash, S. and Vijayakumar, M., “An effective network traffic data control using improved Apriori rule
mining,” Circuits and Systems, Issue 10, Volume 07, pp. 3162-3173, June 2016.
[19] Prakash S, Vijayakumar M, “ Risk assessment in cancer treatment using association rule mining
techniques”, Asian Journal of Research in Social Sciences and Humanities, Volume 6, Issue.10, pp.1031-
1037, 2016.
[20] Preethi, B.C. and Vijayakumar, M. “ A novel Cloud Integration Algorithm(CIA) for Energy Efficient High
Performance Computing Applications in Big Data Multimedia Applications”, Romanian Journal of
Information Science and Technology, Volume 2, No.1, pp. 1-11, March 2018.
[21] Qiong Gu, Zhihua Cai, Li Zhu and Bo Huang, “Data Mining on Imbalanced Data Sets”, IEEE 2008
International Conference on Advanced Computer Theory and Engineering, 2008.
[22] Ravi Arunachalam and Sandipan Sarkar, “The New Eye of Government: Citizen Sentiment Analysis in
Social Media”, IJCNLP 2013 Workshop on Natural Language Processing for Social Media (SocialNLP),
pp. 23–28, 14 October 2013.
[23] Sachira Chinthana Jayasanka and Saminda Premaratne, “Sentiment Analysis for Social Media”, Springer
International Publishing, Switzerland, 2013.
[24] Saranya M and Nithya K, “Campus Navigation and Identifying Current Location through Android Device
to Guide Blind People”, International Research Journal of Engineering and Technology (IRJET), Volume
2, Issue 8, Nov 2015.
[25] Vijayakumar M, Prakash s, “An Improved Sensitive Association Rule Mining using Fuzzy Partition
Algorithm”, Asian Journal of Research in Social Sciences and Humanities, Volume 6, Issue.6, pp.969-981,
2016.
[26] Nandagopal S., Arunachalam V.P., Karthik S."A novel approach for inter-transaction association rule
mining, Journal of Applied Sciences Research VOL, 8, Issue 7, 2012.
[27] Kannan R., Selvambikai M., Jeena Rajathy I., Ananthi S. Rasayan, A study on structural analysis of
electroplated Nano crystalline nickel based thin films, Journal of Chemistry, Vol 10, issue 4, 2017.
[28] Arunvivek G.K., Maheswaran G., Senthil Kumar S., Senthilkumar M., Bragadeeswaran T. Experimental
study on influence of recycled fresh concrete waste coarse aggregate on properties of concrete.
International Journal of Applied Engineering Research, Vol 10, issue 11, 2015
[29] Krishna S.K., Sathya M. Usage of nanoparticle as adsorbent in adsorption process. A review International
Journal of Applied Chemistry, vol 11, Issue 2, 2015.
[30] Sudha S., Manimegalai B., Thirumoorthy P. A study on routing approach for in-network aggregation in
wireless sensor networks, International Conference on Computer Communication and Informatics:
Ushering in Technologies of Tomorrow, Today, ICCCI 2014.
[31] Satheesh A., Jeyageetha V. Improving power system stability with facts controller using certain intelligent
techniques, International Journal of Applied Engineering Research, Vol 9, no 23, 2014.
[32] Ashok V., Kumar N, Determination of blood glucose concentration by using wavelet transform and neural
networks, Iranian Journal of Medical Sciences, Vol 38, Issue 1, 2013.
[33] Somasundaram K., Saritha S., Ramesh K, Enhancement of network lifetime by improving the leach
protocol for large scale WSN, Indian Journal of Science and Technology, Vol 9, Issue 16, 2016.
[34] Jayavel S., Arumugam S., Singh B., Pandey P., Giri A., Sharma A. Use of Artificial Intelligence in
automation of sequential steps of software development / production, Journal of Theoretical and Applied
Information Technology, Vol 57, Issue 3, 2013.
[35] Ramesh Kumar K.A., Balamurugan K., Gnanaraj D., Ilangovan S, Investigations on the effect of flyash on
the SiC reinforced aluminium metal matrix composites, Advanced Composites Letters, Vol 23, Issue 3,
2014.
[36] Suresh V.M., Karthikeswaran D., Sudha V.M., Murali Chandraseker D, Web server load balancing using
SSL back-end forwarding method. IEEE-International Conference on Advances in Engineering, Science
and Management, ICAESM-2012, 2012.
[37] Karthikeswaran D., Sudha V.M., Suresh V.M., Javed Sultan A, A pattern based framework for privacy
preservation through association rule mining, IEEE-International Conference on Advances in Engineering,
Science and Management, ICAESM-2012, 2012.
[38] Senthil J., Arumugam S., Shah P, Real time automatic code generation using generative programming
paradigm, European Journal of Scientific Research, vol. 78, issue 4, 2012.
[39] Vijayakumar J., Arumugam S, Certain investigations on foot rot disease for betelvine plants using digital
imaging technique, Proceedings - 2013 International Conference on Emerging Trends in Communication,
Control, Signal Processing and Computing Applications, IEEE-C2SPCA", 2013.
[40] Vijayakumar J., Arumugam S. Odium piperis fungus identification for piper betel plants using digital
image processing, Journal of Theoretical and Applied Information Technology, vol 60, issue 2, 2014.
[41] Manchula A., Arumugam S, Face and fingerprint biometric fusion: Multimodal feature template matching
algorithm, International Journal of Applied Engineering Research, vol 9, issue 22, 2014.
[42] Ramesh Kumar K.A., Balamurugan K., Arungalai Vendan S., Bensam Raj J, Investigations on thermal
properties, stress and deformation of Al/SiC metal matrix composite based on finite element method.
Carbon - Science and Technology, Vol 6, Issue 3, 2014.
[43] Kanchana A., Arumugam S, Palm print texture recognition using connected-section morphological
segmentation, Asian Journal of Information Technology Vol 6, Issue 3, 2014.
[44] Padmapriya R., Thangavelu P, Characterization of nearly open sets using fuzzy sets, Global Journal of
Pure and Applied Mathematics, vol 11, issue 1, 2015.
[45] P.B. Narandiran, T. Bragadeeswaran, M. Kamalakannan, V. Aravind, Manufacture of Flyash Brick Using
Steel Slag and Tapioca Powder. Jour of Adv Research in Dynamical & Control Systems, Vol. 10, No. 12,
2018, 527-532
[46] R. Girimurugan*, N. Senniangiri, K. Adithya, B. Velliyangiri, Mechanical Behaviour of Coconut Shell
Powder Granule Reinforced Epoxy Resin Matrix Bio Composites, Jour of Adv Research in Dynamical &
Control Systems, Vol. 10, No. 12, 2018, 533-541.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Author
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
