Review of various Models in the Field of Sentiment Analysis

Review of various Models in the Field of Sentiment Analysis.

Author

*1Darren Andrew Dcunha, Department of Computer Science and Engineering, Presidency University

2Dhanush.V Department of Computer Science and Engineering, Presidency University

3Chandu.C.M Department of Computer Science and Engineering, Presidency University

Abstract

This article provides a case study of all sentiment analysis-based deep learning models. One of the most important studies in natural language processing is sentiment analysis, which is frequently employed in politics, news, and other sectors. Given the large number of products purchased through ecommerce websites, it makes sense to drive clients to web pages that showcase the greatest products with the highest ratings and reviews. The notion of sentiment analysis aids in the translation of feedback into numbers, but it also aids in determining whether feedback is favourable or negative in deep learning models. The SLCABG model, for example, combines a large vocabulary with the advantages of deep learning methods. To begin, sensory vocabulary was employed to improve the review's emotional features. It then applies attention to weights and extracts fundamental emotion and contextual data from the review using CNNs and Gated Recurrent Units (GRUs). The SLCABG model improves on the inadequacies of classic sentiment analysis models for product reviews by using sensory vocabulary and deep learning techniques. This approach can also be used to enhance the surfing experience, particularly for websites that track user sentiment.

Keywords: SLCABG model, CNN, BIGRU, GRU, Sentiment analysis