Opinion Mining on Food Services using Topic Modeling and Machine Learning Algorithms

Akila, R.; Revathi, S.; Shreedevi, G.

doi:10.1109/icaccs48705.2020.9074428

Cited by 9 publications

(13 citation statements)

References 8 publications

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“…Currently, the community of scientists has much research on opinion mining methods and the applications of opinion mining at many different levels. 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.…”

Section: Customer Opinion Mining In Online Servicesmentioning

confidence: 99%

“…Today, advanced information technology has changed the way of communication; it helps users easily access information and exchange their opinions about products and services on a large scale in real-time. The advent of social media and review websites allows users to express their opinions (Akila et al, 2020). The explosion of big data has made online community comments or reviews need to be collected and mined automatically, allowing enterprises to track customers' shopping behavior, interests, and satisfaction with products and services (Yadav, 2015;Akter et al, 2016).…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

Sentiment Analysis of Customer Feedback in Online Food Ordering Services

Nguyen

2021

Business Systems Research Journal

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“…The solution to predict the sentiment of customer reviews in FDS domain has evolved from lexicon methods to ML and DL. Several papers [18][19][20][21] have presented the sentiment analysis of customer reviews using lexicon-based, ML and DL techniques in the FDS domain; however a review on DL methods or XAI techniques in the same domain is lacking.…”

Section: Introductionmentioning

confidence: 99%

“…A business needs to rectify its limitations for an enhanced takeaway home delivery system by analysing genuine feedback from customers. Sentiment analysis is the information that comes directly from the customers about their overall experience and opinion about a business, product or service [19]. The experience can be in the form of satisfaction or dissatisfaction and may be positive, negative or neutral [19].…”

Section: Introductionmentioning

confidence: 99%

“…Sentiment analysis is the information that comes directly from the customers about their overall experience and opinion about a business, product or service [19]. The experience can be in the form of satisfaction or dissatisfaction and may be positive, negative or neutral [19]. Also known as opinion mining, sentiment analysis has gained importance over time due to the steep increase in the amount of customer feedback available online in the form of tweets or reviews [20].…”

Section: Introductionmentioning

confidence: 99%

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Sentiment Analysis of Customer Reviews of Food Delivery Services Using Deep Learning and Explainable Artificial Intelligence: Systematic Review

Adak

Pradhan

Shukla

2022

Foods

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During the COVID-19 crisis, customers’ preference in having food delivered to their doorstep instead of waiting in a restaurant has propelled the growth of food delivery services (FDSs). With all restaurants going online and bringing FDSs onboard, such as UberEATS, Menulog or Deliveroo, customer reviews on online platforms have become an important source of information about the company’s performance. FDS organisations aim to gather complaints from customer feedback and effectively use the data to determine the areas for improvement to enhance customer satisfaction. This work aimed to review machine learning (ML) and deep learning (DL) models and explainable artificial intelligence (XAI) methods to predict customer sentiments in the FDS domain. A literature review revealed the wide usage of lexicon-based and ML techniques for predicting sentiments through customer reviews in FDS. However, limited studies applying DL techniques were found due to the lack of the model interpretability and explainability of the decisions made. The key findings of this systematic review are as follows: 77% of the models are non-interpretable in nature, and organisations can argue for the explainability and trust in the system. DL models in other domains perform well in terms of accuracy but lack explainability, which can be achieved with XAI implementation. Future research should focus on implementing DL models for sentiment analysis in the FDS domain and incorporating XAI techniques to bring out the explainability of the models.

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