Sentimental Analysis using Deep Learning Techniques
Kalaivani A1, Thenmozhi D2
1Kalaivani A, SSN College of Engineering, Chennai (Tamil Nadu), India.
2Thenmozhi D, SSN College of Engineering, Chennai (Tamil Nadu), India.
Manuscript received on 05 May 2019 | Revised Manuscript received on 17 May 2019 | Manuscript Published on 23 May 2019 | PP: 600-606 | Volume-7 Issue-6S5 April 2019 | Retrieval Number: F11070476S519/2019©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© 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: 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 platforms via machine-learning (ML) with polarity calculations, sentiment analysis or subjectivity analysis. Sentimental analysis (SA) indicates the text organization which is employed to categorize 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 comprises 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 recent studies regarding the execution of DL models like i) deep neural networks (DNN), ii) deep-belief network (DBN), iii) convolutional neural networks (CNN) together with, iv) recurrent 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), ii) KNN and iii) SVM classification methods. Lastly, the classification methods’ performance is contrasted in respect of accuracy.
Keywords: Sentiment Analysis, Opinion Mining, Deep Learning.
Scope of the Article: Deep Learning