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

Gautam Srivastava

International Journal of Logistics Economics and Globalisation, 2022, vol. 9, issue 3, 205-222

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. (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=120809 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:injleg:v:9:y:2022:i:3:p:205-222

Access Statistics for this article

More articles in International Journal of Logistics Economics and Globalisation from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().