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

Gondhi, Naveen Kumar, Chaahat, -, Sharma, Eishita, Alharbi, Amal H., Verma, Rohit and Shah, Mohd Asif (2022) Efficient Long Short-Term Memory-Based Sentiment Analysis of E-Commerce Reviews. Computational Intelligence and Neuroscience. pp. 1-9. ISSN 1687-5273

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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.

Item Type: Article
Subjects: H Social Sciences > HF Commerce > Marketing > Consumer Behaviour
H Social Sciences > HF Commerce > Electronic Commerce
Divisions: School of Computing > Staff Research and Publications
Depositing User: Clara Chan
Date Deposited: 07 Sep 2022 14:25
Last Modified: 07 Sep 2022 14:25

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