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Sentimental Analysis using Neural Networks
Sharmila Agnal1, Rakshit Baranwal2, Sai Krishna Varun Kumar Rai3, Aviraj Gupta4

1Sharmila Agnal, Department of Computer Science and Engineering ,SRM Institute of Science and Technology.
2Rakshit Baranwal, Department of Computer Science and Engineering ,SRM Institute of Science and Technology.
3Sai Krishna Varun Kumar Rai, Department of Computer Science and Engineering ,SRM Institute of Science and Technology.
4Aviraj Gupta, Department of Computer Science and Engineering ,SRM Institute of Science and Technology.

Manuscript received on October 15, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 1901-1904 | Volume-9 Issue-1, November 2019. | Retrieval Number: A4365119119/2019©BEIESP | DOI: 10.35940/ijitee.A4365.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Sentimental Analysis is to learn about opinion of the consumer that analyzes the understanding of a person or crew of people about a product and present day trends. This task pursuits to learn about the thoughts in a text that leads to a sentimental evaluation by imposing an algorithm to classify opinion. The algorithm that has been used is applied the use of R Language. Social media is one of the biggest elements that we stay with. Twitter is one of the social media that is in the public eye. The undertaking intends to design a sentimental evaluation by using extracting a huge number of tweets. Sentimental Analysis is then employed on this data, which recognizes and distinguishes the wonderful and bad critiques from the impartial tones on the involved topic. This model even extracts the information from newspaper blogs. The data mentioned about any subject matter in the newspaper blogs is precise. The records retrieved from the blogs are additionally analyzed and opinion is acquired. Using this model we gain the most excellent notion.
Keywords: Sentiment Analysis, Text Analysis, R- Studio
Scope of the Article: Neural Information Processing