Framework for Sentimental Analysis of Twitter Data

Authors

  • B. U. Anubharathi  Assistant Professor(SS), Department of Computer Science and Engineering, Rajalakshmi Engineering College, Thandalam,Tamil Nadu, India
  • Aishwarya V  Department of Computer Science, Rajalakshmi Engineering College(Anna university),Thandalam,Tamil Nadu, India
  • S. Aparna  Department of Computer Science, Rajalakshmi Engineering College(Anna university),Thandalam,Tamil Nadu, India
  • S. Divyaalakshmi  Department of Computer Science, Rajalakshmi Engineering College(Anna university),Thandalam,Tamil Nadu, India

DOI:

https://doi.org//10.32628/CSEIT195258

Keywords:

Datamining, Social media, Twitter, Sentimental analysis, Twitter APIs

Abstract

Twitter like Micro-blogging sites has become a wide space for individuals or organizations across the globe to express their views and experience in the form of tweets. The surge of data can be processed using Data mining to obtain further understanding about the public opinions. sentimental analysis is used here to search needs by detecting opinions or emotions from the twitter data. Our results show the cleaned texts of individual tweets using R. Sentimental analysis of any keyword that is given by user is processed. Sentimental analysis used here is helpful in binary classification of tweets i.e. Classification of tweets into positive and negative. Consolidated to this we also analyse Multiple sentiments of the tweets. We likewise break down most extreme recurrence of catchphrase utilized in the tweets and its users. Trending hashtags according to location using location ID and pattern match technique is utilized in finding the recurrence of hashtags utilized in a tweet of explicit end client.

References

  1. Ms.Umaa Ramakrishnan and Ms. Rashmi Shankar MCA V Semester, Mr.Ganesha K Lecturer, Department Of Computer Science, Amrita Vishwa Vidyapeetham, Mysore Campus, Sentiment analysis of twitter data based on user based behaviour, 2015.
  2. Study and Analysis of Data mining Algorithms for Healthcare Decision Support System Monali Dey, Siddharth Swarup Rautaray Computer School of KIIT University, Bhubaneswar ,India,2014
  3. International Journal of Innovative Research in Science, Engineering and Technology (An ISO 3297: 2007 Certified Organization) Vol. 5, Issue 8, August 2016 Copyright to IJIRSET DOI:10.15680/IJIRSET.2016.0508032 14538 A Survey of Health Care Prediction Using Data Mining Sujatha R 1 , Sumathy R 2 , Anitha Nithya R 3 Assistant Professor, Dept. of Computer Science Engineering, Sri Krishna College of Technology, Coimbatore, India.
  4. Chuan-Jun Su and Jorge A.Quan Yon, “Sentiment Analysis and Information Diffusion On Social Media:The case of the Zika virus,” International Journal of Information and Education Technology ,Sentiment Analysis and Information Diffusion on Social Media: The Case of the Zika Virus, 2018.
  5. Chenfei Sun ; Qingzhong Li ; Lizhen Cui ; Hui Li ; Yuliang Shi, Heterogeneous network-based chronic disease progression mining, 2018
  6. Erin Hea-Jin Kim Yoo Kyung Jeong, Yuyoung Kang, Min Song, Department of Library and Information Science, Yonsei University, Korea, Topic based content and Sentimental Analysis of Ebola virus on Twitter and in the News, 2015.
  7. Sunil Kumar and Maninder Singh, Big Data Analytics for healthcare Industry , 2018.
  8. Prof. GVNKV Subbarao1 , Prof.MD Sameeruddin Khan2 , K.VIJAY KUMAR3 1,2,3 CSE Department, Sree Dattha Institute of Engineering & Science, Case Study on Data Mining Application in Health Care Monitoring Systems, May 2016.
  9. Pieter Noordhuis, Micheal Heijkoop, Alexander Lazovik, Mining Twitter in the cloud : A case study, 2010.
  10. Apoor V Agarwal Jasnee t Singh Sabharwal2 Columbia University, NY,U.S.A.(2) Guru Gobind Singh Indraprastha University, New Delhi, India, End-to-End sentiment Analysis of Twitter Data, 2012.
  11. Chenguang Kong Department of Computer Science, Georgia State University, Atlanta, GA 30309, USA Guangchun Luo School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China Ling Tian School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China Xiaojun Cao Department of Computer Science, Georgia State University, Atlanta, GA 30309, USA, Disseminating authorized content via data analysis in opportunistic social networks,2018.
  12. HENFENG LEI, SHUANGYUANYANG, HAN LIU,(Member,IEEE), SABA ASLAM,(Member,IEEE), JINYU LIU, HALEFOM TEKLE, EMMANUEL BUGINGO, AND DEFU ZHANG, (Member,IEEE) University of science and Technology, Hong Kong, Mining of Nutritional Ingredients in Food for Disease Analysis, 2018.
  13. Andrea Vanzo and Danilo Croce and Roberto Basili Department of Enterprise Engineering University of Roma Tor VergataVia del Politecnico 1,00133 Roma Italy, Context based model for Sentimental Analysis in Twitter, 2016.
  14. Ankita Gupta, Jyotika Pruthi, Neha Sahu, Ankita Gupta, Student, Dept. Of Computer Science THE NORTHCAP UNIVERSITY, Sector 23A Gurgaon, Haryana(122017), INDIA, Sentimental analysis of tweets using Machine Learning Approach, 2017.
  15. Shubham S.Deshmukh, Harshal Joshi, Pranali Pandhare, Aniket M.Junghare 1,2,3,4BE in Computer engg. ZCOER, Pune, Maharashtra, India, Twitter data Analysis using R, International Journal of Science, Engineering and Technology Research(IJSETR)Volume 6,Issue 4, April 2017,ISSN:2278 – 7798, 2017.
  16. Varsha Sahayak Vijay Shete Apashabi Pathan BE (IT) BE (IT) ME(Computer)Department of Information Technology, Savitribai Phule Pune University, Pune, India, Sentiment Analysis of Twitter Data International Journal of Innovative Research in Advanced Engineering(IJIRAE) Issue 1, Volume 2, 2015.
  17. Chenguang Kong Ling Tian School of (Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China Xiaojun Cao Department of Computer science, Georgia state University, Atlanta, GA 30309,USA, Disseminating authorized content via data analysis in opportunistic social networks, 2018.
  18. James Spencer and Gulden Uchyigit. School of Computing , Engineering and Mathematics University of Brighton,Brighton,BN2 4GJ.2014 International Conference on Information Technology and Multimedia(ICIMU).November 18-20, 2014,Putrajaya,Malaysia, Sentimentor: Sentiment Analysis of Twitter Data, 2014.
  19. Zoubida Alaoui Mdaghri, Mourad El Yadari, Abdelillah Benyoussef, Abdellah El Kenz Faculty of Science Rabat Morocco, Rabat ,Study and analysis of Data Mining for Healthcare, 2016.
  20. Mohammed Issa M.A1-Kharusi Abubakar Usman Jamilu Awwalu, Development of a Smart Environment for sentimental analysis, Data analysis and New Knowledge Mining, 2012.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2019-04-30

Issue

Volume 5, Issue 2, March-April-2019

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
B. U. Anubharathi, Aishwarya V, S. Aparna, S. Divyaalakshmi, " Framework for Sentimental Analysis of Twitter Data, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 2, pp.345-354, March-April-2019. Available at doi : https://doi.org/10.32628/CSEIT195258