An Insight on Sentiment Analysis Research from Text using Deep Learning Methods
D. Christy Daniel1, L. Shyamala2
1D. Christy Daniel, Research Scholar, School of Computing Science and Engineering, VIT Chennai Campus, Chennai, 600127, India.
2L. Shyamala, Associate Professor, School of Computing Science and Engineering, VIT Chennai Campus, Chennai, 600127, India.
Manuscript received on 02 July 2019 | Revised Manuscript received on 09 July 2019 | Manuscript published on 30 August 2019 | PP: 2033-2048 | Volume-8 Issue-10, August 2019 | Retrieval Number: J93160881019/2019©BEIESP | DOI: 10.35940/ijitee.J9316.0881019
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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: Nowadays, Deep Learning (DL) is a fast growing and most attractive research field in the area of image processing and natural language processing (NLP), which is being adopted across several sectors like medicine, agriculture, commerce and so many other areas as well. This is mainly because of the greater advantages in using DL like automatic feature extraction, capability to process more number of parameters and capacity to generate more accuracy in results. In this paper, we have examined the research works which have used the DL based Sentiment Analysis (SA) for the social network data. This paper provides the brief explanation about the SA, the necessities of the pre-processing of text, performance metrics and the roles of DL models in SA. The main focus of this paper is to explore how the DL algorithms can enhance the performance of SA than the traditional machine learning algorithms for text based analysis. Since DL models are more effective for NLP research, the text classification can be applied on the complex sentences in which there are two inverse emotions which produces the two different emotions about an event. Through this literature appraisal we conclude that by using the Convolutional Neural Network (CNN) technique we can obtain more accuracy than others. The paper also brings to the light that there is no major focus on mixed emotions by using DL methods, which eventually increases the scope for future researches.
Keywords: Sentiment Analysis, Deep Learning, Machine Learning, Neural Networks.
Scope of the Article: Deep Learning