Efficient Long Short-Term Memory-Based Sentiment Analysis of E-Commerce Reviews.

Publisher: Hindawi Pub. Corp Country of Publication: United States NLM ID: 101279357 Publication Model: eCollection Cited Medium: Internet ISSN: 1687-5273 (Electronic) NLM ISO Abbreviation: Comput Intell Neurosci Subsets: MEDLINE

Original Publication: New York, NY : Hindawi Pub. Corp.

Memory, Short-Term* ; Sentiment Analysis*; Commerce ; Consumer Behavior ; Humans ; Publications

In today's modern era, e-commerce is making headway through the process of bringing goods within everyone's grasp. Consumers are not even required to step out of the comfort of their homes for buying things, which makes it very convenient for them. Moreover, there is a wide variety of brands to choose from. Since more customers depend on online shopping platforms these days, the value of ratings is also growing. To buy these products, people rely solely on the reviews that are being provided about the products. To analyze these reviews, sentiment analysis needs to be performed, which can prove useful for both the buyers and the manufacturer. This paper describes the process of sentiment analysis and its requirements. In this paper, Amazon Review dataset 2018 has been used for carrying out our research and Long Short-Term Memory (LSTM) has been combined with word2vec representation, resulting in improving the overall performance. A gating mechanism was used by LSTM during the training process. The proposed LSTM model was evaluated on four performance measures: accuracy, precision, recall, and F1 score, and achieved overall higher results when compared with other baseline models.
Competing Interests: The authors declare that they have no conflicts of interest.
(Copyright © 2022 Naveen Kumar Gondhi et al.)

Date Created: 20220627 Date Completed: 20220628 Latest Revision: 20220628

20240105

PMC9232314

10.1155/2022/3464524

35755767