Downloads: 3 | Weekly Hits: ⮙1 | Monthly Hits: ⮙1
Research Paper | Computer Science & Engineering | India | Volume 10 Issue 4, April 2021
Sentiment Analysis of Twitter Data to Analyze the Acceptance of Aadhaar
Aarushi Thakral, Vasantha W. B.
Abstract: India is a developing nation and investing 1.4 billion US Dollars is bound to have a huge impact on the economic health of the country, more so, if it has been invested into a single project, namely the Aadhaar project being implemented by UIDAI. However, it has been touted that this investment will pay itself off in terms of its applicability in various services, governmental as well as non-governmental. This project aims to analyze the general public perception of Aadhaar across the nation. Moreover, the twitter dataset being processed has been divided into two categories namely: developed and under-developed, on the basis of Gross Domestic Product (GDP) of the States and Union Territories of India. Equal number of tweets have been studied from both the categories of States and Union Territories to be able to place both the categories on a level field. The basic phenomenon of the needs of developing states differing from those of underdeveloped states has been exploited, as such sentiments would be expressed in the tweets from those locations. These tweets are assumed to reflect the opinions of the people living in that particular region. After close analysis, the findings of this project have been represented using various visualization techniques.
Keywords: UIDAI, Aadhaar, GDP, Sentiment Analysis
Edition: Volume 10 Issue 4, April 2021,
Pages: 1213 - 1218
How to Cite this Article?
Aarushi Thakral, Vasantha W. B., "Sentiment Analysis of Twitter Data to Analyze the Acceptance of Aadhaar", International Journal of Science and Research (IJSR), https://www.ijsr.net/get_abstract.php?paper_id=ART20181492, Volume 10 Issue 4, April 2021, 1213 - 1218
How to Share this Article?
Similar Articles with Keyword 'Sentiment Analysis'
Emotion Detector and Counsellor Chatbox
Furnishing Efficient Opinion from Federation of Context and Emoticons
Priya C, Sankar Ganesh K