Data Extraction and Sentimental Analysis from “Twitter” using Web Scrapping
Mehul Jain1, Sushmit Vaish2, Manas Patil3, Gawas Mahadev Anant4
1Mehul Jain*, B. Tech student, Department of Computer Science and Engineering Vellore Institute of Technology, Vellore India.
2Sushmit Vaish, B. Tech student, Department of Computer Science and Engineering Vellore Institute of Technology, Vellore India.
3Manas Patil, B. Tech student, Department of Computer Science and Engineering Vellore Institute of Technology, Vellore India.
4Dr. Mahadev A. Gawas, Associate Professor Department of Computer Science and Engineering, VIT Vellore India completed his Ph. D from the Department of Computer Science & Information Systems, BITS Pilani, India
Manuscript received on September 23, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 6451-6455 | Volume-9 Issue-1, October 2019 | Retrieval Number: A2226109119/2019©BEIESP | DOI: 10.35940/ijeat.A2226.109119
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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: In this paper , we attempt to do the sentimental analysis of the 2016 US presidential elections. Sentimental analysis requires the data to be extracted from websites or sources where people present their opinions, views ,complaints about the subjects that need to analyzed .Furthermore, it is necessary to ensure that the sample size of the data is large enough to get conclusive results .It is also necessary to ensure that the data is cleaned before it is used to make predictions. Cleaning is done using common techniques like tokenization, spell check ,etc. Sentimental Analysis is one of the by-products of Natural Language Processing . This paper includes data collection as well as classification of textual data based on machine learning.
Keywords: Sentimental Analysis, Web Scrapping ,Web Extraction, Classification Of Data using Machine Learning Algorithms.