Sentiment Analysis on E-Commerce Apparels using Convolutional Neural Network
DOI:
https://doi.org/10.47839/ijc.21.2.2592Keywords:
Deep Learning, Convolutional Neural Networks, Sentiment Analysis, Long Short Term Memory (LSTM), Word2Vec, Term Frequency – Inverse Document Frequency (TF-IDF)Abstract
The Fourth Industrial Revolution (4.0) is a fusion of advances in Artificial Intelligence (AI), Robotics, the Internet of Things (IoT), Genetic Engineering, Quantum Computing, and other technologies. A large number of people are using internet-based services as a result of enhanced internet infrastructure and decreased costs. As a result, such businesses' attempts to penetrate internet media are disrupted. The e-commerce company, like Amazon, offers both customer-to-customer and business-to-business services in the apparel sector. Companies must understand the needs of buyers to maximize their profits. As a result, consumer sentiment analysis is carried out. However, because this procedure is time-consuming, it is made automatically utilizing artificial intelligence approaches. According to the findings of a study on sentiment analysis on an E-Commerce-based web store for women, the apparels review dataset using the CNN method with the word vector generator and TF-IDF can produce a higher accuracy of 94%.
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