Twitter Sentimental Analysis using Deep Learning Techniques

Authors

  • Dr. Pamela Vinitha Eric   New Horizon College of Engineering, Bengaluru, Karnataka, India
  • Anu Priya K R  New Horizon College of Engineering, Bengaluru, Karnataka, India

Keywords:

Sentiment analysis, Opinion mining, Deep learning.

Abstract

There is a rapid growth in the domain of opinion mining as well as sentiment analysis which targets to discover the text or opinions present on the disparate social media plat- forms via machine-learning (ML) with polarity calculations, sentiment analysis or subjectivity analysis. Sentimental analysis (SA) indicates the text organization which is employed to cate- gorize the expressed feelings or mindset in diverse manners like favorable, thumbs up, positive, unfavorable, thumbs down, negative, etc. SA is a demanding and notable task that compris- es i) natural-language processing (NLP), ii) web mining and iii) ML. Also, to tackle this challenge, the SA is merged with deep learning (DL) techniques since DL models are efficient because of their automatic learning ability. This paper emphasizes re- cent studies regarding the execution of DL models like i) deep neural networks (DNN), ii) deep-beliefnetwork (DBN), iii) convolutional neural networks (CNN) together with, iv) re- current neural network (RNN) model. Those DL models aid in resolving different issues of SA like a) sentiment classification, b) the classification methods of i) rule-based classifiers(RBC), KNN and iii) SVM classification methods. Lastly, the classi- fication methods’ performance is contrasted in respect of accu- racy.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2020-08-30

Issue

Volume 6, Issue 4, July-August-2020

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Research Articles

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How to Cite

[1]
Dr. Pamela Vinitha Eric , Anu Priya K R, " Twitter Sentimental Analysis using Deep Learning Techniques" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 4, pp.381-867, July-August-2020.