Abstract
Governments, multilateral agencies like the World Bank, United Nations, and Development Banks as well as other nonprofits are involved in a variety of developmental activities across the world. A lot of resources are spent to ensure proper consultations and post-implementation verification of results. But this does not completely ensure whether the objectives are achieved. The new web technologies provided methodologies and developed tools that allow the users to pool resources on projects over the Internet. Social media allowed real-time feedback for citizens, monitoring developmental initiatives of Governments and multilateral agencies. The role of technology ensures that the consultations and ongoing feedback can be captured, analyzed, and used in understating the stakeholder reactions to the project and its implementation. This helps in making necessary course corrections avoiding costly mistakes and overruns. In this paper, we model a tool to monitor, study, and analyze popular feedback, using forums, social media, surveys, and other crowdsourcing techniques. The feedback is gathered and analyzed using both quantitative and qualitative methods to understand what crowd is saying. The summation and visualization of patterns are automated using text mining and sentiment analysis tools including text analysis and tagging/annotation. These patterns provide insight into the popular feedback and sentiment effectively and accurately than the conventional method. The model is created by integrating such feedback channels. Data is collected and analyzed, and the results are presented using tools developed in open-source platform.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Choo E, Yu T, Chi M (2015) Detecting opinion spammer groups through community discovery and sentiment analysis. In: IFIP annual conference on data and applications security and privacy. Springer International Publishing, pp 170–187
Dave K, Lawrence S, Pennock DM (2003) Mining the peanut gallery: opinion ex-traction and semantic classification of product reviews. In: Proceedings of the 12th international conference on World Wide Web. ACM, pp 519–528
Fang X, Zhan J (2015) Sentiment analysis using product review data. J Big Data 2(1):5
Pang B, Lee L, Vaithyanathan S (2002) Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10. Association for Computational Linguistics, pp 79–86
Hu M, Liu B (2004) Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pp 168–177
Liu B (2007) Web data mining: exploring hyperlinks, contents, and usage data. Springer Science & Business Media
Liu B (2015) Sentiment analysis: mining opinions, sentiments, and emotions. Cambridge University Press
Hoogervorst R, Essink E, Jansen W, van den Helder M, Schouten K, Frasincar F, Taboada M (2016) Aspect-based sentiment analysis on the web using rhetorical structure theory. In: International conference on web engineering. Springer International Publishing, pp 317–334
Popescu AM, Etzioni O (2007) Extracting product features and opinions from reviews. In: Natural language processing and text mining. Springer, London, pp 9–28
Turney PD (2002) Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th annual meeting on association for computational linguistics. Association for Computational Linguistics, pp 417–424
Open Calais. Retrieved Feb 16, 2017, from http://www.opencalais.com/
Extract Meaning from your Text. Retrieved Feb 16, 2017, from http://www.textrazor.com/
IBM Watson—AlchemyAPI. Retrieved Feb 16, 2017, from http://www.alchemyapi.com/
Text Analytics from Saplo. Retrieved Feb 12, 2017, from http://www.saplo.com/
The Qualitative Data Analysis & Research Software. Retrieved Feb 12, 2017, from http://www.atlasti.com/
NVivo product range | QSR International. Retrieved Feb 16, 2017, from http://www.qsrinternational.com/products-nvivo.aspx
Software ? Stanford Named Entity Recognizer (NER). Retrieved Feb 16, 2016, from http://nlp.stanford.edu/software/CRF-NER.shtml
Apache Tika, Retrieved March 16, 2016, from http://tika.apache.org/
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Mini, U., Nair, V., Poulose Jacob, K. (2019). Monitoring Public Participation in Multilateral Initiatives Using Social Media Intelligence. In: Mishra, D., Yang, XS., Unal, A. (eds) Data Science and Big Data Analytics. Lecture Notes on Data Engineering and Communications Technologies, vol 16. Springer, Singapore. https://doi.org/10.1007/978-981-10-7641-1_17
Download citation
DOI: https://doi.org/10.1007/978-981-10-7641-1_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-7640-4
Online ISBN: 978-981-10-7641-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)