An NLP Technique on Sentiment Analysis

Author:

,Attri Aadesh,Rai Alok, ,Malhotra Yash,

Abstract

We have to structure the data which was given to us from the Twitter social media for accurate analysis and make something outof it. We will be finding the sentiment behind the given comment by a user on twitter so that we can sort out the meaning of the text. To get the negative emotions of the text, we will be using different algorithms to find the intention behind it. Fathom this kind of issue, estimation investigation and profound learning methods are two combining methods. We are using Naive Bayes algorithms, SVM (Support Vector Machine) and otherclassification algorithms to get our required output.These are known deep learning /Machine Learning ways to extract the feelings in sentences. At the end of the result we will get the desired output and we will check the accuracy of our output accordingly.

Publisher

Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP

Reference16 articles.

1. S. W. Davenport, S. M. Bergman, J. Z. Bergman, J. Z., and M. E. Fearrington, "Twitter versus Facebook: Exploring the role of narcissism in the motives and usage of different social media platforms." Computers in Human Behavior 32, pp. 212-220, 2014. https://doi.org/10.1016/j.chb.2013.12.011

2. A. Esuli, and F. Sebastiani, "Sentiwordnet: A publicly available lexical resource for opinion mining", in Proceedings of LREC, vol. 6, pp. 417- 422, May 2006.

3. A. Go, R. Bhayani, and L. Huang, L., "Twitter sentiment classification using distant supervision", CS224N Project Report, Stanford, 1-12, 2009. R. Kohavi, and F. Provost, "Glossary of terms" Machine Learning 30(2-3), pp 271-274, 1998.

4. A. C. Lima, and L. N. (CASoN), 2012 Fourth. 52- 57, IEEE, 2012.

5. M. C. De Marneffe, B. MacCartney, and C. D. Manning, "Generating typed dependency parses from structure parses", in Proceedings of LREC, vol. 6, pp. 449-454, 2006.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3