Total views : 383

Advocacy Monitoring of Women and Children Health through Social Data

Affiliations

  • Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore - 641112, Tamil Nadu, India

Abstract


Background/Objective: To classify the extracted women and children health data from the social media and to utilize it for advocacy monitoring. Methods/Statistical Analysis: Advocacy monitoring can be performed by extracting the social data related to women and children health. A keyword based search technique is used for this purpose. The children health details like the nutrition deficiencies, lack of vaccination, diseases like pneumonia, diarrhea and malaria that affect new born children and the women health data like maternal weight loss, maternal mortality rate, sanitation and antenatal care during maternity can be gathered from the social media using keyword based search technique. The extracted data are needed to be analyzed and classified into related data groups using Decision tree C4.5 and Support Vector Machine (SVM). Findings: Decision tree C4.5 algorithm classifies the data based on the concept of information entropy. The data are classified at each node of the tree after analyzing the attribute of the data. SVM analyzes the extracted data and uses the health parameters listed to group the related data. The approach is of two stages: training and testing. The training dataset is build using the health data representing the listed search words. This training set is used to classify the test data. The data are tested with the training set and only women and child health data are stored in classes that help in advocacy monitoring in an efficient way. Applications/Improvements: Advocacy monitoring is required to define the socio-economic status of a region. The proposed approach efficiently classifies the extracted social data of women and children health and aids in effective advocacy monitoring.

Keywords

Advocacy Monitoring, Decision Tree C4.5, Search Approach, Support Vector Machine

Full Text:

 |  (PDF views: 229)

References


  • O’Flynn M. Tracking progress in Advocacy: Why and how to monitor and evaluate Advocacy projects and programs. International NGO Training and Research Centre (INTRAC); 2009. p. 1–12.
  • Jayanag B, Vineela K, Vasavi S. A study on feature subsumption for sentiment classification in social networks using natural language processing. International Journal of Computer Applications. 2012 Sep; 53(18):29–33.
  • Westert GP, Schellevis FG, de Bakker DH, Groenewegen PP, Bensing JM, Van der Zee J. Monitoring health inequalities through general practice: The Second Dutch National Survey of General Practice. The European Journal of Public Health. 2005 Feb; 15(1):59–65.
  • Corley CD, Mikler AR, Singh KP, Cook DJ. Monitoring influenza trends through Mining Social Media. BIOCOMP; 2009. p. 1–7.
  • Lampos V, Cristianini N. Tracking the flu pandemic by monitoring the social web. IEEE 2nd International Workshop on Cognitive Information Processing (CIP); Elba. 2010 Jun 14-16. p. 411–6.
  • Kolkur S, Jayamalini K. Web data extraction using tree structure algorithms – A comparison. IJRTE. 2013 Jul; 2(3):35–9.
  • Carvalho JP, Pedro VC, Batista F. Towards intelligent mining of public social networks’ influence in society. IFSA Joint World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS); Edmonton, AB. 2013 Jun 24-28. p. 478–83.
  • Erhart G, Matula VC, Skiba D. Method of automatic customer satisfaction monitoring through social media. U.S. Patent Application. 12/777. 2010.
  • Pampalon R, Hamel D, Gamache P. A comparison of individual and area-based socio-economic data for monitoring social inequalities in health. Component of Statistics Canada. 2009 Sep; 20(3):85–94.
  • Antonopoulos N, Veglis A, Gardikiotis A, Kotsakis R, Kalliris G. Web third-person effect in structural aspects of the information on media websites. Computers in Human Behavior. 2015 Mar; 44(1):48–58.
  • Gottipati S, Jiang J. Finding thoughtful comments from social media. COLING; 2012. p. 995–1010.
  • Zhao P, Li X, Wang K. Feature extraction from micro-blogs for comparison of products and services. Web Information Systems Engineering – WISE. Springer, Berlin Heidelberg. 2013; 8180:82–91.
  • Nivedha R, Sairam N. A machine learning based classification for social media messages. Indian Journal of Science and Technology. 2015 Jul; 8(16):1–4.

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.