A Survey of Deep Learning Techniques in the Field of Sentiment Analysis for the Hindi Language

Vijay Kumar Soni*, Smita Selot**
* Department of Computer Science and Engineering, Chandigarh University, Punjab, India.
** Department of Computer Applications, Shri Shankaracharya Technical Campus, Bhilai, India.
Periodicity:March - May'2022
DOI : https://doi.org/10.26634/jcom.10.1.18752

Abstract

In the domain of natural language processing, sentiment analysis is an important field for any review. Nowadays Indian languages are more popular for any product review. In North India, Hindi is the most widely used language. People having Hindi as their mother tongue can easily express their opinions and thoughts through that language. In the field of research, Hindi language has many challenges due to very less research work, limited data for analysis and less size of corpus data. Deep learning techniques are currently used for predicting feelings. Recurrent Neural Network (RNN), particularly the Long Short Term Memory (LSTM) and Convolution Neural Network (CNN), are two generally used deep learning approaches. Depending on the domain area of application, the strategies are utilized in combinations or as stand-alone procedures. This review paper emphases on the numerous flavours of deep learning approaches employed in various applications of sentiment analysis at the sentence and aspect levels.

Keywords

Natural Language Processing (NLP), Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), Convolution Neural Network (CNN), Deep Learning (DL), Sentiment Analysis (SA).

How to Cite this Article?

Soni, V. K., and Selot, S. (2022). A Survey of Deep Learning Techniques in the Field of Sentiment Analysis for the Hindi Language. i-manager’s Journal on Computer Science, 10(1), 27-36. https://doi.org/10.26634/jcom.10.1.18752

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