Open AccessDeep Learning Approach of Product Evaluation Using Comment Analysis
Author(s) -
Syed Mudasar
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.39382
Subject(s) - computer science , deep learning , purchasing , product (mathematics) , artificial intelligence , data science , graph , process (computing) , machine learning , click through rate , sentiment analysis , big data , the internet , data mining , information retrieval , world wide web , marketing , theoretical computer science , business , mathematics , geometry , operating system
Digital reviews now play a critical role in strengthening global consumer communications and influencing consumer purchasing patterns. Consumers can use e-commerce giants like Amazon, Flipchart, Snap deal, Jio and others to share their experiences and provide real insights about the performance of a product to future buyers. The classification of reviews into positive and negative sentiment is required in order to derive relevant insights from a big set of reviews. Comment Analysis is a computer programme that extracts subjective data from text. Out of Various Classification models Deep Learning Approach of Product Evaluation Using Comment Analysis is to develop a model that uses AI technologies like Deep Learning to process thousands and millions of online reviews on a product in a split second of time and rate the products on a scale of 1-5 based on the user comments We have worked on two deep learning models based on Recurrent Neural Networks (RNN) and Graph Convolution Network (GCN). Keywords: LSTN, GCN, NLTK