Survey on Election Prediction Using Machine Learning Technique
Abstract & Details
Research Area
Computer Engineering
Keywords
Election
social media
Twitter
prediction
twitter data
NLP
MLT
lemmatization
OS platforms
LDA
Naive Bayes
Artificial Intelligence
Decision Tree
Sentiment Analysis
Information Retrieval
Algorithm
Abstract
In today’s era educational organization strongly needs devices which are ready to access and use and also various operating system platforms are required for different purpose. Another important thing is many educational organizations still used Virtual machine method to used different OS platforms for various operations. This project come up with the tweets data from the and analyzing the results from the tweets. Newspaper is the crucial part of the human life. Because newspaper is a source of information. Every type of information is present there on the newspaper. But now a day’s life becomes very easy because of the social media. People can use social media according to their convenience anywhere anytime. Social media is one of the important parts of the human life. Social media keeps every generation updated. There are many social sites with people are connected. Twitter and face book are the famous social media among the generation. People get influence with the social media. Whatever is happening over the world, social media keeps you in connect. So, the people are more incline toward the social media. When about elections, if any data related on any elections party. People started reading and thinking on that. In the form of comments, they will share their opinion that what are their thoughts about the elections and the political parties. Some people are in support with or some are in oppose of. So, the above scenario, from the social media which political party is on top. Predictions of elections from the social media from the twitter data we are predicting the election outcomes.
License
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Author Information
| # | Name | Institute / Affiliation |
|---|---|---|
| 1 | Tejas Kolambe | Genba Sopanrao Moze College of Engineering, Balewadi, Pune |
| 2 | Prof. Sangeeta Alagi | Genba Sopanrao Moze College of Engineering, Balewadi, Pune |
| 3 | Vishal Bibe | Genba Sopanrao Moze College of Engineering, Balewadi, Pune |
| 4 | Karan Gite | Genba Sopanrao Moze College of Engineering, Balewadi, Pune |
| 5 | Sanket Chachar | Genba Sopanrao Moze College of Engineering, Balewadi, Pune |
How to Cite
Use the following formats to cite this article in your research.
APA Style
Kolambe, Tejas, Alagi, Prof. Sangeeta, Bibe, Vishal, Gite, Karan, & Chachar, Sanket (2023). Survey on Election Prediction Using Machine Learning Technique. International Journal of Advance Research and Innovative Ideas In Education, 9(2), 787-793.
MLA Style
Kolambe, Tejas, et al. "Survey on Election Prediction Using Machine Learning Technique." International Journal of Advance Research and Innovative Ideas In Education, vol. 9, no. 2, 2023, pp. 787-793.
IEEE Style
Tejas Kolambe, Prof. Sangeeta Alagi, Vishal Bibe, Karan Gite, and Sanket Chachar, "Survey on Election Prediction Using Machine Learning Technique," International Journal of Advance Research and Innovative Ideas In Education, vol. 9, no. 2, pp. 787-793, 2023.
Vancouver Style
Kolambe Tejas, Alagi Prof. Sangeeta, Bibe Vishal, Gite Karan, Chachar Sanket. Survey on Election Prediction Using Machine Learning Technique. International Journal of Advance Research and Innovative Ideas In Education. 2023;9(2):787-793.
Harvard Style
Kolambe, Tejas, Alagi, Prof. Sangeeta, Bibe, Vishal, Gite, Karan, & Chachar, Sanket (2023) 'Survey on Election Prediction Using Machine Learning Technique', International Journal of Advance Research and Innovative Ideas In Education, 9(2), pp. 787-793.
Chicago Style
Kolambe, Tejas, et al. "Survey on Election Prediction Using Machine Learning Technique." International Journal of Advance Research and Innovative Ideas In Education 9, no. 2 (2023): 787-793.
Turabian Style
Kolambe, Tejas, et al. "Survey on Election Prediction Using Machine Learning Technique." International Journal of Advance Research and Innovative Ideas In Education 9, no. 2 (2023): 787-793.
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