Discovery of Probable Sentiments in Hypertensive Pregnant Women using Horizontal Fragmentation and Pointwise Mutual Information

Authors

  • Dr. Sudhir Tirumalasetty  Department of Computer Science and Engineering, Vasireddy Venkatadri Institute of Technology, Guntur, Andhra Pradesh, India
  • P. Tejaswini  Department of Computer Science and Engineering, Vasireddy Venkatadri Institute of Technology, Guntur, Andhra Pradesh, India
  • R Renuka  Department of Computer Science and Engineering, Vasireddy Venkatadri Institute of Technology, Guntur, Andhra Pradesh, India
  • M Naga Sirisha  Department of Computer Science and Engineering, Vasireddy Venkatadri Institute of Technology, Guntur, Andhra Pradesh, India

DOI:

https://doi.org//10.32628/CSEIT1952191

Keywords:

Sentiment Analysis, Horizontal Fragmentation, Pointwise Mutual Information

Abstract

Since a decade research over sentiment analysis and opinion mining was evolving slowing and emerging widely with greater perspectives and objectives. Sentiment analysis is an important task in order to gain insights over the huge amounts of opinions that are generated on a daily basis. This analysis relies on the opinions made by the individuals. These opinions are text, may be positive or negative or a phrase which gives significance to the context. Also these opinions have the power of expressing the context besides drags the attention of new folks. Expressing such opinions ranges from documents level, to the sentence level, to phrase level, to word level and to special symbol level. All these opinion types are labelled with common name Sentiment Analysis. Sentiment Analysis is health care is evolving narrowly with wider research strings. This paper mainly focuses in identifying Sentiments in health care. These sentiments can be medical test values which may be numeric and nominal; sometimes in text too. Such sentiments are identified with pre-fragmentation of data set and Pointwise Mutual Information measure. To accomplish this data of hypertensive pregnant women is considered.

References

  1. Cane W.K. Leung & Stephen C.F. Chan. Sentimental Analysis of Product Reviews. The Hong Kong Polytechnic University, Hong Kong SAR, 2008.
  2. Yustinus Eko Soelistio and Martinus Raditia Sigit Surendra. Simple Text Mining For Sentiment Analysis of Political Figure Using Naive Bayes Classier Method. CoRR, 2015.
  3. Jyoti Jain, Asst. Prof. Archana Shinde & Prachi Panchal, Sentiment Analysis using Machine Learning. Department of Information Technology, Sinhgad Academy of Engineering, Maharashtra, India, 2016.
  4. Krishnaveni K S, Rohit R Pai and Vignesh Iyer, "Faculty Rating System Based on Student Feedbacks Using Sentimental Analysis", 978-1-5090-6367-3/17/, IEEE, 2017.
  5. E.J. Fortuny, T.D. Smedt, D. Martens & W. Daelemans , Media Coverage In Times of Political Crisis: A Text Mining Approach, Expert Systems with Applications, Sciendirect, 2012.
  6. G.F. Luger. Artificial Intelligence Structures And Strategies For Complex Problem Solving, Pearson Education Inc., 182-185., 2009.
  7. Yamanishi, K., & Li, H. Mining open answers in questionnaire data. IEEE Intelligent Systems, 17(5),pp. 58-63, 2002.
  8. Jiawei Han, Micheline Kamber & Jian Pei. Data Mining Concepts and Techniques. Waltham: Morgan Kaufmann Publishers, 2012.
  9. Liu, B., Hsu, W., & Ma, Y. Integrating classification and association rule mining. Proceedings of Knowledge Discovery and Data Mining, pp. 80-86, 1998.
  10. C. P. Prathibhamol, Ashok, A. Solving multi label problems with clustering and nearest neighbor by consideration of labels, Advances in Intelligent Systems and Computing, vol. 425, pp. 511-520, 2016.

Downloads

Published

2019-04-30

Issue

Section

Research Articles

How to Cite

[1]
Dr. Sudhir Tirumalasetty, P. Tejaswini, R Renuka, M Naga Sirisha, " Discovery of Probable Sentiments in Hypertensive Pregnant Women using Horizontal Fragmentation and Pointwise Mutual Information , IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 2, pp.742-745, March-April-2019. Available at doi : https://doi.org/10.32628/CSEIT1952191