Collective tweet analysis for accurate user sentiment analysis - a case study with Delhi Assembly Election 2015 Online publication date: Fri, 28-Sep-2018
by Lija Mohan; M. Sudheep Elayidom
International Journal of Big Data Intelligence (IJBDI), Vol. 5, No. 4, 2018
Abstract: Social media postings range from the environment and politics to technology and the entertainment industry. Since this can be construed as a form of collective wisdom, the authors decided to investigate its power at predicting the real-world outcomes. The objective was to design a keyword-aware user-based collective tweet mining approach to identify the opinion of each user, which is proved to be more accurate compared to the sentiment analysis done to each tweet. To make our application scalable, MapReduce programming on a Hadoop distributed processing framework is utilised. From the analysis done on 2015 Delhi Assembly Elections case study, we correctly predicted that Aam Admy Party has a higher support compared to the existing ruling party, BJP. Also, we compared our sentiment analysis algorithm with other existing techniques and identified that ours is efficient in terms of space and time complexity which makes it suitable for other BigData applications.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Big Data Intelligence (IJBDI):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook