phat 18(16): e2

Research Article

Opinion Mining for the Tweets in Healthcare Sector using Fuzzy Association Rule

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  • @ARTICLE{10.4108/eai.13-7-2018.159861,
        author={Mamta  Mittal and Iqbaldeep  Kaur and Subhash  Chandra  Pandey and Amit  Verma and Lalit  Mohan  Goyal},
        title={Opinion Mining for the Tweets in Healthcare Sector using Fuzzy Association Rule},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology},
        keywords={Health, Social Media Analysis, Twitter Mining, Sentimental Analysis, Text Mining, Association Rule},
  • Mamta Mittal
    Iqbaldeep Kaur
    Subhash Chandra Pandey
    Amit Verma
    Lalit Mohan Goyal
    Year: 2018
    Opinion Mining for the Tweets in Healthcare Sector using Fuzzy Association Rule
    DOI: 10.4108/eai.13-7-2018.159861
Mamta Mittal1, Iqbaldeep Kaur2, Subhash Chandra Pandey3, Amit Verma2, Lalit Mohan Goyal4,*
  • 1: Department of Computer Science & Engineering, G.B. Pant Govt. Engg. College, Okhla, New Delhi, India
  • 2: Department of Computer Science & Engineering, Chandigarh Group of Colleges, Mohali, India
  • 3: Computer Science & Engineering Department, Birla Institute of Technology, Mesra, Ranchi(Patna Campus) Patna, Bihar, India
  • 4: Department of CE, J.C. Bose University of Science & Technology, YMCA, Faridabad, India
*Contact email:


Communication among several internet users has become more convenient through social networking sites to where each user sharing his own opinions on different matters, such as Healthcare, Education, marketing etc. The Objective of this paper is to present a method to make it easier for even a layman to predict and analyze one’s health issues on his own by making use of tweets on the social website As far as methodology or techniques is concerned, an algorithm has been framed for the same to perform the analysis on health care tweets with association rules to classify the ailments and their symptoms using a corpus through fuzzy set and two step approach for Document Term Matrix & Term Document Matrix. The results demonstrate the comparison of different terms over the WordCloud which concludes that in this novel approach of two step authentication the average accuracy of association between the hiv ailments is 98% through correlation table and association between the HIV ailments with 98% correlation.