Sentiment based Food Classification for Restaurant Business

Hegde, Sindhu B; Satyappanavar, Supriya; Setty, Shankar

doi:10.1109/icacci.2018.8554794

Cited by 7 publications

(6 citation statements)

References 8 publications

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“…The FDS common complaint types described in Table 3 can be categorised into four common groups (delivery time, customer service, food quality and cost) [19,37] as shown in Table 4. Organisations can channel the concerned department to address the issues and increase customer satisfaction to promote their brand or product.…”

Section: Resultsmentioning

confidence: 99%

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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Section: Resultsmentioning

confidence: 99%

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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“…Conventional text-mining techniques have been widely adopted to extract semantic representations from text in conventional textual-informationbased restaurant recommendation tasks. For example, Hegde et al (2018) extracted consumers' top-n satisfied foods from four classified courses by first applying the TF-IDF and bag of words (BoW) techniques to a cleaned online review for feature extraction. Thereafter, sentiment analysis was performed to classify positive and negative reviews using a support vector machine technique.…”

Section: Related Workmentioning

confidence: 99%

“…In other words, using only rating information renders the prediction of consumer preferences ineffective. Thus, most studies have used textual information, such as online reviews and restaurant attributes, to address sparsity issues and effectively estimate consumer preferences for restaurants (Asani et al , 2021; Hegde et al , 2018). Conventional text-mining techniques have been widely adopted to extract semantic representations from text in conventional textual-information-based restaurant recommendation tasks.…”

Section: Related Workmentioning

confidence: 99%

“…Based on this, text mining, such as term frequency-inverse document frequency (TF-IDF) and topic models, is applied to extract a semantic text representation. The similarities between the extracted semantic representations are then calculated or integrated into the CF model to predict consumer preferences (Asani et al , 2021; Hegde et al , 2018). This approach may be effective than using only ratings information to predict consumer preferences.…”

Section: Introductionmentioning

confidence: 99%

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Restaurant recommendation model using textual information to estimate consumer preference: evidence from an online restaurant platform

Jang

Kim

et al. 2023

JHTT

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Purpose Textual information about restaurants, such as online reviews and food categories, is essential for consumer purchase decisions. However, previous restaurant recommendation studies have failed to use textual information containing essential information for predicting consumer preferences effectively. This study aims to propose a novel restaurant recommendation model to effectively estimate the assessment behaviors of consumers for multiple restaurant attributes. Design/methodology/approach The authors collected 1,206,587 reviews from 25,369 consumers of 46,613 restaurants from Yelp.com. Using these data, the authors generated a consumer preference vector by combining consumer identity and online consumer reviews. Thereafter, the authors combined the restaurant identity and food categories to generate a restaurant information vector. Finally, the nonlinear interaction between the consumer preference and restaurant information vectors was learned by considering the restaurant attribute vector. Findings This study found that the proposed recommendation model exhibited excellent performance compared with state-of-the-art models, suggesting that combining various textual information on consumers and restaurants is a fundamental factor in determining consumer preference predictions. Originality/value To the best of the authors’ knowledge, this is the first study to develop a personalized restaurant recommendation model using textual information from real-world online restaurant platforms. This study also presents deep learning mechanisms that outperform the recommendation performance of state-of-the-art models. The results of this study can reduce the cost of exploring consumers and support effective purchasing decisions.

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“…al. [2] suggest a system of recommendations feedback from customers in forecasting star ratings for food classification for restaurant business. Positive, unfavourable, moderate, and divisive user feedback are grouped into four groups.…”

Section: Literature Surveymentioning

confidence: 99%

Food Reviews Classification using multi-label convolutional neural network text classifier

Lasne¹,

Nandrekar²,

Khan³

et al. 2021

ITM Web Conf.

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Most commercial websites, such as Amazon, encourage users to leave reviews of the goods and services they get after purchasing them. For certain consumers, this analysis is critical when determining whether or not to buy a product. Understanding the consequences of feedback and correctly classifying their utility may therefore be an advantageous method for websites. The classification results can also be used as a review and recommendation program for ongoing success. Nowadays people visit several restaurants on various occasions. They get confused most of the time after having a look at the food menu. Based on the ratings and reviews of the dish it becomes easier for them to decide the dish they wish to order. However they unable to read each review of the previous customers. So to overcome this issue, we have proposed NLP (Natural Language Processing) technique and Spacy CNN (Convolutional Neural Network) pipeline system which will classify all the reviews in a single rating. Each review is labelled with a reviewer's score indicating the sentiment of the reviewers. Our task is to predict a reviewer’s score on a scale of 0 or 1. Where 1 indicates the users like the dish while 0 indicates that the reviewers were not satisfied with the dish.

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Sentiment based Food Classification for Restaurant Business

Cited by 7 publications

References 8 publications

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

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

Restaurant recommendation model using textual information to estimate consumer preference: evidence from an online restaurant platform

Food Reviews Classification using multi-label convolutional neural network text classifier

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