Sentiment Analysis of Food Reviews Using User Rating Score

Patel, Rutvi; Sornalakshmi, K.

doi:10.1007/978-981-15-5329-5_39

Cited by 1 publication

(2 citation statements)

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“…In the study of Akila et al (2020) and Nagpal et al (2020), the authors have proposed tools and methods to collect and analyze customer comments using machine learning and topic models. In another study by Patel et al (2020), the author analyzed users' emotions based on the customer rating score of the products and services they used in the food services. From the results of domestic and foreign researches, the author found that there are two popular approaches in opinion mining:…”

Section: Customer Opinion Mining In Online Servicesmentioning

confidence: 99%

“…However, after randomly reviewing the content of the collected comment dataset and based on the results of the rating (the rating field in the dataset), founding that comments with a rating less than 5.0 have a negative meaning, and vice versa, comments with a rating equals or greater than 5.0 have a positive meaning. To perform the data labeling process before being trained, the research applied the classifying emotions method according to the customer rating (Liu, 2017;Patel et al, 2020) to divide the collected dataset into 2 datasets, labeled according to the following rules:…”

Section: Data Labelingmentioning

confidence: 99%

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Sentiment Analysis of Customer Feedback in Online Food Ordering Services

Nguyen

2021

Business Systems Research Journal

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Background: E-commerce websites have been established expressly as useful online communication platforms, which is rather significant. Through them, users can easily perform online transactions such as shopping or ordering food and sharing their experiences or feedback. Objectives: Customers’ views and sentiments are also analyzed by businesses to assess consumer behavior or a point of view on certain products or services. Methods/Approach: This research proposes a method to extract customers’ opinions and analyse sentiment based on a collected dataset, including 236,867 online Vietnamese reviews published from 2011 to 2020 on foody.vn and diadiemanuong.com. Then, machine learning models were applied and assessed to choose the optimal model. Results: The proposed approach has an accuracy of up to 91.5 percent, according to experimental study findings. Conclusions: The research results can help enterprise managers and service providers get insight into customers’ satisfaction with their products or services and understand their feelings so that they can make adjustments and correct business decisions. It also helps food e-commerce managers ensure a better e-commerce service design and delivery.

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Section: Customer Opinion Mining In Online Servicesmentioning

confidence: 99%

Section: Data Labelingmentioning

confidence: 99%