Understanding Customer Sentiment: Lexical Analysis of Restaurant Reviews

Ara, Jinat; Hasan, Md. Toufique; Omar, Abdullah Al; Bhuiyan, Hanif

doi:10.1109/tensymp50017.2020.9230712

Cited by 9 publications

(6 citation statements)

References 12 publications

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“…Dengan mempertahankan tingkat kepuasan pelanggan sangat penting untuk menjaga bisnis restoran dengan baik [5], karena tujuan yang ideal dari restoran di antaranya menyenangkan pelanggan [6]. Bagi manajer restoran, ulasan positif yang diposting oleh pelanggan restoran dapat digunakan untuk meningkatkan reputasi atau kualitas pada restoran itu sendiri [7].…”

Section: Pendahuluanunclassified

Analisis Sentimen Berbasis Aspek Ulasan Pelanggan Restoran Menggunakan LSTM Dengan Adam Optimizer

2023

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Consumers believe that restaurant reviews are very important when choosing a restaurant. Due to the fact that reviews have become one of the most effective ways to influence customer decisions, research that has been done on restaurant customer reviews is about sentiment analysis. Previous studies have only used sentiment analysis at the sentence or document level, while a better level uses Aspect-Based Sentiment Analysis (ABSA), or a type of aspect-based sentiment analysis. LSTM is a variant of RNN that stores long-term information in memory cells. Use of global max pooling to reduce output resolution features and prevent overfitting. In addition, the optimization method used by Adam Optimizer is an adaptive learning rate optimization algorithm specifically designed to train deep neural networks. This study aims to classify restaurant customer opinions based on aspects (food, place, service, and price) based on restaurant customer reviews on Indonesian-language TripAdvisor with LSTM and global max pooling for sentiment classification (negative, half negative, neutral, half positive, positive). The results of this study indicate that the ABSA in restaurant customer reviews for sentiment classification accuracy is 78.7% and the aspect category accuracy is 78%, both are interconnected and can help understand restaurant customer opinions on TripAdvisor.

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Section: Pendahuluanunclassified

Analisis Sentimen Berbasis Aspek Ulasan Pelanggan Restoran Menggunakan LSTM Dengan Adam Optimizer

2023

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“…Service, missing item, problem with order, missing order, rude service [4,15,19,[32][33][34] Food, food quality, food taste [4,15,19,[32][33][34] Place, location [19,27,35] Experience, environment, ambiance, dining atmosphere [4,15,27,35,36] Value for money, restaurant value, cost [4,15,27,35,36] Time, slow service, slow delivery [19,33] There have been several comprehensive and systematic review papers published over the decade that describe topic categorisation using various techniques applied in sentiment analysis; hence, this paper will not describe those methods again and instead focus more on ML/DL and XAI techniques. This section describes previous findings on implementing ML and DL models for sentiment analysis in the FDS domain.…”

Section: Complaint Types Referencesmentioning

confidence: 99%

“…In the food industry, customers often look into restaurant reviews before placing their orders. Nowadays, restaurants or food delivery services (FDSs) have a review or feedback system that is integrated in their portal or social media platforms; however, only a few act on customer opinions due to the presence of a large amount of review data across various platforms and the lack of customer service consultants that will go through each of these comments and act on them [4]. At present, organisations need not depend on customer service consultants to read all the reviews because they can rely on artificial intelligence (AI) to solve their problems and save costs.…”

Section: Introductionmentioning

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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“…NLP plays an important role in ABSA for restaurant reviews, employing advanced machine learning and linguistic algorithms to break down complex, unstructured textual data into meaningful components [8]. This method enables the extraction of specific aspects such as food quality, service, ambiance, and others, providing a comprehensive understanding of customer sentiments [9]. By delving into different aspects in a text review, NLP helps identify positive and negative sentiments, facilitating targeted improvements to enhance the overall dining experience.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Spatiotemporal Aspect-Based Sentiment Analysis for Chain Restaurants using Machine Learning

Kawtar,

Fathi,

Assad

et al. 2024

IJACSA

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In recent years, aspect-based sentiment analysis of restaurant business reviews has emerged as a pivotal area of research in natural language processing (NLP), aiming to provide detailed analytical methods benefiting both consumers and industry professionals. This study introduces a novel approach, Hierarchical Spatiotemporal Aspect-Based Sentiment Analysis (HISABSA), which combines lexicon-based methods such as VADER Lexicon, the AFFIN model, and TextBlob with contextual methods. By integrating advanced machine learning (ML) techniques, this hybrid methodology facilitates sentiment analysis, empowering chain restaurants to assess changes in sentiments towards specific aspects of their services across different branches and over time. Leveraging transformer-based models such as RoBERTa and BERT, this approach achieves effective sentiment classification and aspect extraction from text reviews. The results demonstrate the reliability of extracting valid aspects from online reviews of specific branches, offering valuable insights to business owners striving to succeed in competitive markets.

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Understanding Customer Sentiment: Lexical Analysis of Restaurant Reviews

Cited by 9 publications

References 12 publications

Analisis Sentimen Berbasis Aspek Ulasan Pelanggan Restoran Menggunakan LSTM Dengan Adam Optimizer

Analisis Sentimen Berbasis Aspek Ulasan Pelanggan Restoran Menggunakan LSTM Dengan Adam Optimizer

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

Hierarchical Spatiotemporal Aspect-Based Sentiment Analysis for Chain Restaurants using Machine Learning

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