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Title: A study on reviews of online grocery stores during COVID-19 pandemic using sentiment analysis

Authors: Gautam Srivastava

Addresses: GL Bajaj Institute of Management and Research, Knowledge Park – III, Greater Noida, India

Abstract: Digitalisation is playing a very crucial role in India during the COVID-19 lockdown. Grocery items are one of the essential commodities needed by the people during the lockdown. The sales of online grocery stores rose abnormally during the pandemic and stores faced a lot of problems while delivering grocery items to consumers. It becomes very difficult for them to deliver the products on time and maintain the satisfaction level of the consumes. During that time, huge online reviews were posted by consumers on different digital platforms. This study focussed on analysing those reviews and developing a supervised machine learning model. Sentiment analysis is used to develop the classification model. TF-IDF followed by naïve Bayes classification techniques is used to do the sentiment analysis. The developed model helps the online grocery stores to deal with huge online reviews and segment the consumers based on their positive and negative reviews.

Keywords: sentiment analysis; naïve Bayes classification; TF-IDF; online grocery stores; pandemic.

DOI: 10.1504/IJLEG.2022.120809

International Journal of Logistics Economics and Globalisation, 2022 Vol.9 No.3, pp.205 - 222

Received: 20 Feb 2021
Accepted: 17 Oct 2021

Published online: 10 Feb 2022 *

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