CNN Based Framework for Sentiment Analysis of Tweets
Vikas Tripathi1, Bhasker Pant2, Vijay Kumar3
1Dr. Vikas Tripathi, Department of Computer Science and Engineering, Graphic Era deemed to be university, Dehradun, India.
2Dr. Bhasker Pant, Department of Computer Science and Engineering, Graphic Era deemed to be university, Dehradun, India.
3Dr. Vijay Kumar, Department of Physics, Graphic Era Hill University, Dehradun.
Manuscript received on 01 June 2019 | Revised Manuscript received on 07 June 2019 | Manuscript Published on 04 July 2020 | PP: 88-90 | Volume-8 Issue- 4S3 March 2019 | Retrieval Number: D10190384S319/2019©BEIESP | DOI: 10.35940/ijitee.D1019.0384S319
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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: The project deals with the problem of visual updates on twitter; that differentiates tweets according to the context in which they are exposed: good or bad. Twitter is an online small-scale platform for showcasing different thoughts perceptions related to any area, news, information etc. It is an informal communication framework that allows clients to compose brief information of 280 characters long. It’s a quickly developing assistance with more than 400 million enlisted clients of which 326 million individuals are dynamic and half of them sign on twitter each day – creating almost 500 million tweets every day. Considering this huge measure of spending we want to pick up the declaration of open assessment by examining the feelings communicated in the tweets. Investigating general assessment is fundamental for any kind of business. firms trying to identify the appropriate response of their items in the market, foreseeing political decisions and anticipating financial occasions, for example, stock costs. The purpose of this project is to develop an effective working class for accurate and automated segmentation of the tweet stream.
Keywords: CNN, Tweets, Sentiment analysis, Machine Learning.
Scope of the Article: Computer Science and Its Applications