Fissare: Troubleshooting Tool in Improving Government Application Systems using Social Listening Concept


Authors
  • Dianne Nicole G. Gabriel
  • Rosicar E. Escober
  • Open University, Polytechnic University of the Philippines
Published in


Abstract
  • Government agencies deploy different application systems all over the country. Every agency has its own application systems to deploy which conform to the mandate that they have. With those application systems, implementing agencies most likely will seek help in implementation and maintenance. Same goes with the deployment agency; they must continuously support the systems being implemented to ensure that the client gets the full-function of the systems.


Keywords
  • Knowledge-Based System, Social Listening, Sentiment Analysis, Data Mining




Cite As
  • APA 7th Edition:
    Gabriel, D., & Escober, R. (2019). Fissare: Troubleshooting Tool in Improving Government Application Systems using Social Listening Concept. Innovatus, 2(1), 77-82.
  • Harvard:
    Gabriel, D. and Escober, R., 2019. Fissare: Troubleshooting Tool in Improving Government Application Systems using Social Listening Concept. Innovatus, 2(1), pp.77-82.
  • IEEE:
    [1] D. Gabriel and R. Escober, "Fissare: Troubleshooting Tool in Improving Government Application Systems using Social Listening Concept", Innovatus, vol. 2, no. 1, pp. 77-82, 2019.


References
  • "A Guide to Knowledge Based Systems | Smartsheet", Smartsheet, 2018. [Online]. Available: https://www.smartsheet.com/knowledge-base-systems-and-templates. [Accessed: 21- Feb- 2019].
  • "Expert Systems", 2018.
  • P. Sajja and R. Akerkar, "Knowledge-Based Systems for Development", 2018. [Accessed 21 February 2018].
  • The Fundamentals of Social Media Analytics. Crimson Hexagon.
  • "Social media listening: Your launchpad to success on social", Sprout Social. [Online]. Available: https://sproutsocial.com/insights/guides/social-media-listening/. [Accessed: 21- Feb- 2019].
  • P. Whatman, "A Beginner's Guide to Social Listening", The Mention Blog, 2018. [Online]. Available: https://mention.com/blog/social-listening/. [Accessed: 20- Feb- 2018].
  • "Sentiment Analysis | Lexalytics", Lexalytics. [Online]. Available: https://www.lexalytics.com/technology/sentiment. [Accessed: 21- Feb- 2018].
  • W. Fan and M. Gordon, "The power of social media analytics", Communications of the ACM, vol. 57, no. 6, pp. 74-81, 2014. Available: 10.1145/2602574.
  • E. Rolland and M. Farhadloo, "Fundamentals of Sentiment Analysis and Its Applications", 2016. [Accessed 21 February 2018].
  • "What is Data Mining (Predictive Analytics, Big Data)?", Statsoft.com. [Online]. Available: http://www.statsoft.com/Textbook/Data-Mining-Techniques#mining. [Accessed: 21- Feb- 2018].
  • "What is the difference between big data and data mining?", Technopedia. [Online]. Available: https://www.techopedia.com/7/29678/technology-trends/what-is-the-difference-between-big-data-and-data-mining. [Accessed: 21- Feb- 2018].
  • H. Bayer, M. Aksogan, E. Celik and A. Kondiloglu, "Big data mining and business intelligence trends", Journal of Asian Business Strategy, vol. 7, no. 1, pp. 23-33, 2017. Available: 10.18488/journal.1006/2017.7.1/1006.1.23.33.
  • L. Torgo, "A Brief Introduction to Data Mining", 2017. [Accessed 21 February 2018].
  • "Learn Data Mining - Data Mining Tutorials - DataFlair", DataFlair. [Online]. Available: https://data-flair.training/blogs/category/data-mining/. [Accessed: 21- Feb- 2018].
  • I. Etikan, "Comparison of Convenience Sampling and Purposive Sampling", American Journal of Theoretical and Applied Statistics, vol. 5, no. 1, p. 1, 2016. Available: 10.11648/j.ajtas.20160501.11.


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