Restaurant Reviews Research Articles

AXCF: Aspect‐based collaborative filtering for explainable recommendations

AbstractWith the rapid growth of the e‐commerce market facilitated, users are often overwhelmed by the excessive online information, making item selection challenging. While recommendation services have significantly enhanced user experience and sales, these traditional models often overlook the complexity of user‐item interactions and user preferences based on various item aspects. The proposed AXCF framework innovatively combines graph‐based collaborative filtering (CF), which captures high‐order connectivity between users and items, with aspect‐based sentiment analysis (ABSA) to extract detailed user preferences from online reviews. This approach addresses the limitations of linear relationships in traditional CF models by incorporating deep neural networks and introduces a method to overcome the cold‐start problem using online reviews as auxiliary information. By focusing on main aspects such as food, ambiance, and service derived from restaurant reviews, AXCF provides personalized recommendations with improved accuracy and explanatory power, demonstrating its superiority over existing models through experimental results. This study not only presents a significant advancement in recommender systems but also highlights the importance of high‐order connectivity and aspect‐based preferences in understanding and catering to user needs in the e‐commerce platform.

Meta heuristic approaches for sentiment analysis

AbstractOn the Internet, online microblogging and social networks have experienced tremendous growth. Millions of individuals share opinions on social networking platforms including Twitter, Facebook, YouTube, and Microblogging sites. Millions of individuals express their thoughts on social platforms namely Twitter, Facebook, YouTube, and microblogging sites. As user‐generated content is growing to a huge extent on the internet, the analysis, extraction identification and classification of opinion and sentiment polarity of different aspects has now become a challenging issue in sentiment analysis. In sentiment analysis, aspect level opinion mining, opinion spam review detection, extraction and representation of most relevant accessed data from the user‐generated data is a difficult process. The word vector representation using existing deep learning techniques affects the performance in aspect level opinion mining. Spammers propagate false opinion for payment exchange, which leads to degradation of the financial growth, business, and fame of the organization. To overcome the limitations aspect level opinion mining is used to recognize sentiment and polarity of aspects in the specified text. This work presents various input feature vectors of deep learning approaches aiming to improve performance in aspect level opinion mining. Next, three meta‐heuristic algorithms with k‐means clustering approach for classification of opinion spam reviews on social media transit tweets is presented. The proposed approaches are evaluated and compared with other existing approaches as well as other benchmark datasets namely Restaurant and Amazon reviews.

Extracting and classifying psychoacoustic features from online restaurant reviews

Recent work has shown that poor acoustic conditions persist in many restaurants. Owners who receive negative reviews of their establishment's soundscape may struggle to interpret the subjective customer responses into actionable corrective measures. Therefore, further work is needed to taxonomize the acoustically relevant keywords and phrases that occur in user reviews. In this study, an open-source database of restaurant, bar, and coffee shop reviews from across the United States has been obtained. Sentiment analysis and keyword count are used to extract positive, negative, and neutral subjective reviews and subjective features related to acoustics. The resulting subjective features are then categorized, weighed, and linked to objective acoustic parameters using machine learning techniques. Results from this study suggest that owners and consultants may be able to utilize online customer reviews to monitor acoustical comfort and ascertain the nature of an acoustical problem, not only in restaurants, but in any business sector where customers submit online user reviews.

Computerized Content Analysis of TripAdvisor Reviews: A Case Study of a Street Food Restaurant With One Michelin Star

Online review platform is a digital communication for reviewers to communicate with future customers. TripAdvisor reviews of a Thai street food restaurant were examined in the periods of before (2012-2017) and after (2018-2023) being awarded one Michelin Star. Two digital tools were employed: Web Scraper and KH Coder. Positive aspects of the reviews were found. Most of the reviews rose from 4 bubble ratings at 42.53% during 2012-2017 to 5 bubble ratings at 42.43% during 2018-2023. Frequent words and co-occurrence network highlighted the signature dishes of the restaurant and quality of food as well as pinpointed the need for a table reservation and the waiting time. The findings are significant to the customer service experience, specifically pertaining to dining and table assistance. Future studies can expand to multiple online restaurant reviews or a nation’s cuisine using computerized content analysis.

“Authentic and Amazing”: authenticity as an evaluative category in online consumer restaurant reviews

Claims and evaluations of authenticity are a powerful resource in food discourse: reviewers use evaluations of authenticity to demonstrate their expertise, and restaurants viewed as authentic receive higher star ratings. But the multivalent nature of authenticity presents challenges for researchers. This contribution seeks to understand authenticity by combining computational and corpus driven discourse analysis methods. O’Connor et al. (2017) sought to quantify the impact of authenticity on consumer perception via four theoretical authenticity types (type, craft, moral, and idiosyncratic). This method is tested using a sample of US restaurant reviews and compared to sentiment analysis metrics computed from the same dataset. All types except for moral authenticity showed a positive effect on sentiment. Authenticity in restaurant reviews is further investigated by examining collocates of terms referring to authenticity and compiling keywords of subcorpora created from high and low scoring reviews. Reviewers most often topicalize authenticity in terms of place, taste, and descriptors of ethnicity. These findings illustrate how combining corpus driven discourse analytical and computational methods can illuminate evaluation from multiple perspectives and provide insights which may help to improve computational approaches in the future.

Negative Peer Feedback and User Content Generation: Evidence From a Restaurant Review Platform

Social interactions are becoming increasingly central to the day-to-day operations of digital platforms. In this study, we investigate how negative peer feedback shapes user behavior in an online review platform. Leveraging fine-grained digital trace data of users throughout their tenure on the platform, we find interesting insights regarding the impact of negative peer feedback. First, negative feedback improves user retention on the platform relative to users who receive no feedback at all. Moreover, for users who are retained and write their next reviews, we find that negative feedback improves review frequency and quality. These novel findings demonstrate that, contrary to the conventional wisdom, negative peer feedback can benefit the platform's welfare as it increases user engagement and retention, and that no feedback can be a worse alternative.

The investigation related to the influence of dimension manipulation on regret performance based on the upper confidence bound algorithm

This paper aims to investigate the effects of dimension manipulation on the performance of a recommendation algorithm applied to a dataset of restaurant reviews. In this paper, the Zomato dataset which contains restaurant reviews and other relevant information in Bangalore City was used. This paper extracted ratings that each user gave for each restaurant from the list of user feedback for each restaurant. These different ratings were stored as a core feature that was used for the restaurant recommendation algorithm to estimate the true mean rating of each restaurant. Upper Confidence Bound bandit algorithm was used as the restaurant recommendation algorithm to find the restaurant with the highest average rating in the dataset. Dimension raising and dimension reduction were used as ways to manipulate dimension in this paper. Principal Component Analysis was used as the technique to reduce feature dimension in the dataset. It reduced features into two principal components and the first principal component was used in place of the original core feature. Dimension raising was implemented based on the original core feature and another feature that correlated with it the most. The product of these two features was used in place of the original core feature. The experimental results suggest that dimension manipulation leads to decreased regret performance when employing the Upper Confidence Bound algorithm for recommendation. Intriguingly, within the realm of dimension manipulation, dimension reduction exhibited a more adverse impact on regret performance compared to dimension raising.

Sentiment Analysis in Portuguese Restaurant Reviews: Application of Transformer Models in Edge Computing

This study focuses on improving sentiment analysis in restaurant reviews by leveraging transfer learning and transformer-based pre-trained models. This work evaluates the suitability of pre-trained deep learning models for analyzing Natural Language Processing tasks in Portuguese. It also explores the viability of utilizing edge devices for Natural Language Processing tasks, considering their computational limitations and resource constraints. Specifically, we employ bidirectional encoder representations from transformers and robustly optimized BERT approach, two state-of-the-art models, to build a sentiment review classifier. The classifier’s performance is evaluated using accuracy and area under the receiver operating characteristic curve as the primary metrics. Our results demonstrate that the classifier developed using ensemble techniques outperforms the baseline model (from 0.80 to 0.84) in accurately classifying restaurant review sentiments when three classes are considered (negative, neutral, and positive), reaching an accuracy and area under the receiver operating characteristic curve higher than 0.8 when examining a Zomato restaurant review dataset, provided for this work. This study seeks to create a model for the precise classification of Portuguese reviews into positive, negative, or neutral categories. The flexibility of deploying our model on affordable hardware platforms suggests its potential to enable real-time solutions. The deployment of the model on edge computing platforms improves accessibility in resource-constrained environments.

Improving Sentiment Classification on Restaurant Reviews Using Deep Learning Models

Natural Language processing (NLP) includes the task of classifying texts. The proposed paper uses the ability and power of machine learning and deep learning techniques for sentiment analysis on restaurant reviews. With the increasing use of social media, classification of reviews has significant role in the development of public opinion. This work has extensive applications from client opinion study, market research and social media monitoring. The machine learning algorithms and NLP techniques are used classifying reviews and the results are rigorously analysed. It was noted that results obtained from logistic regression and multinomial naïve bayes algorithms are better than other machine learning models. Hence these two algorithms are selected to optimize further. After tuning the hyperparameters by grid search technique, the best accuracy obtained by logistic regression and multinomial naive bayes on validation dataset was 89.9% and 89.6% respectively. Deep learning architectures were also utilized for reviews classification. The accuracy obtained by Convolutional Neural Net (CNN) and Bi-Directional Long-short-term-memory (Bi-LSTM) was 89% and 90% respectively.

Unveiling factors influencing judgment variation in sentiment analysis with natural language processing and statistics.

TripAdvisor reviews and comparable data sources play an important role in many tasks in Natural Language Processing (NLP), providing a data basis for the identification and classification of subjective judgments, such as hotel or restaurant reviews, into positive or negative polarities. This study explores three important factors influencing variation in crowdsourced polarity judgments, focusing on TripAdvisor reviews in Spanish. Three hypotheses are tested: the role of Part Of Speech (POS), the impact of sentiment words such as "tasty", and the influence of neutral words like "ok" on judgment variation. The study's methodology employs one-word titles, demonstrating their efficacy in studying polarity variation of words. Statistical tests on mean equality are performed on word groups of our interest. The results of this study reveal that adjectives in one-word titles tend to result in lower judgment variation compared to other word types or POS. Sentiment words contribute to lower judgment variation as well, emphasizing the significance of sentiment words in research on polarity judgments, and neutral words are associated with higher judgment variation as expected. However, these effects cannot be always reproduced in longer titles, which suggests that longer titles do not represent the best data source for testing the ambiguity of single words due to the influence on word polarity by other words like negation in longer titles. This empirical investigation contributes valuable insights into the factors influencing polarity variation of words, providing a foundation for NLP practitioners that aim to capture and predict polarity judgments in Spanish and for researchers that aim to understand factors influencing judgment variation.

Analysis of Almaty’s Restaurant Reviews through Topic Modelling

Analysis of Almaty’s Restaurant Reviews through Topic Modelling

SBiLM: Siamese Bi-LSTM model for handling imbalance in fake review detection

The growing significance of online reviews in e-commerce platforms has a profound impact on consumers' purchase decisions. Unfortunately, dishonest sellers exploit this system by engaging in deceptive practices and manipulating reviews. These fake reviews are very hard to be detected by humans. Hence, the issue of detecting fake online reviews has gained significant attention due to their prevalence. Typically, the datasets available have much higher number of real reviews as compared to fake reviews, which leads to data imbalance problem. Machine learning algorithms struggle to learn from these datasets and give biased results. However, existing studies on this topic have not adequately addressed the challenges posed by imbalanced data. They have mostly used under-sampling or over-sampling to handle imbalance in datasets, which leads to potential information loss or overfitting due to replication. Hence, to handle this class imbalance problem without using sampling techniques, this paper proposes a novel SBiLM model using Siamese Neural Network with Bidirectional LSTM and Manhattan Distance. SBiLM takes pair of reviews as inputs and computes similarity between them using distance metric. The outcomes indicate that the SBiLM model exhibits better performance in terms of both accuracy and loss, when compared with traditional deep learning models on a highly imbalanced restaurant review dataset. This presents a promising strategy for tackling the issues related to data imbalance in the domain of fake review detection.

Comparative Restaurant Review Analysis Using Deep Learning

Comparative Restaurant Review Analysis Using Deep Learning

Fake Google restaurant reviews and the implications for consumers and restaurants

Fake Google restaurant reviews and the implications for consumers and restaurants

Analyzing Restaurant Reviews to Predict Customer Satisfaction Trends

Numerous studies comparing the quality of various restaurants' cuisines have been conducted. Area, average cost for two people, votes, cuisines, mostly flavour, and restaurant type are some of the criteria used to evaluate restaurants. Finding out which restaurants people like to eat at and reading reviews of such places is the primary objective here. The goal of this study is to develop a model that can foretell if a review of the eatery will be favourable or negative. A number of prediction algorithms, including Multinomial Naive Bayes, SVC, XGB Regressor, Pipeline, and Logistic Regression, will be utilised to achieve this goal. Finally, we'd like to identify the "best" model that can forecast the reviewer's emotional tone.

A Model of Continual and Deep Learning for Aspect Based in Sentiment Analysis

Sentiment Analysis is a useful tool in several social and business contexts. Aspect Sentiment Classification is a subtask in Sentiment Analysis that gives information about features or aspects of people, entities, products, or services present in reviews. Different Deep Learning models have been proposed to solve Aspect Sentiment Classification focus on a specific domain such as restaurant,hotel, or laptop reviews. However, there are few proposals for creating a single model with high performance in multiple domains. The Continual Learning approach with neural networks has been used to solve aspect classification in multiple domains. However, avoid low aspect classification performance in Continual Learning is challenging. As a consequence, potential neural networkweight shifts in the learning process in different domains or datasets.In this paper, a novel Aspect Sentiment Classification approach is proposed. Our approach combines a Transformer Deep Learning technique with a Continual Learning algorithm in different domains. The input layer used is the pre‐trained model Bidirectional Encoder Representations from Transformers. The experiments show the efficacy of our proposal with 78 .

A Model of Continual and Deep Learning for Aspect Based in Sentiment Analysis

Sentiment Analysis is a useful tool in several social and business contexts. Aspect Sentiment Classification is a subtask in Sentiment Analysis that gives information about features or aspects of people, entities, products, or services present in reviews. Different Deep Learning models have been proposed to solve Aspect Sentiment Classification focus on a specific domain such as restaurant,hotel, or laptop reviews. However, there are few proposals for creating a single model with high performance in multiple domains. The Continual Learning approach with neural networks has been used to solve aspect classification in multiple domains. However, avoid low aspect classification performance in Continual Learning is challenging. As a consequence, potential neural networkweight shifts in the learning process in different domains or datasets.In this paper, a novel Aspect Sentiment Classification approach is proposed. Our approach combines a Transformer Deep Learning technique with a Continual Learning algorithm in different domains. The input layer used is the pre‐trained model Bidirectional Encoder Representations from Transformers. The experiments show the efficacy of our proposal with 78 .

Evaluation of Machine Learning Approach for Sentiment Analysis using Yelp Dataset

Due to the abundance of text data representing public opinion, the Sentiment Analysis study is getting more and more important. Various techniques and methods have been proposed to address the issues. One of those techniques is deep learning algorithms which have been used to achieve great results in Natural Language Processing (NLP) applications. Sentiment Analysis is a part of NLP application that extracts emotional information from texts. In this study, we investigate the performance of sequence-based model, i.e., LSTM, compared with multi-layer perceptron Neural Network (NN) to classify the polarity of the text review based on negative or positive. The dataset used in this study is a restaurant review taken from the Yelp website. The dataset is trained using Word2vec word embedding to convert words contained in the dataset into numerical vector representation which is used as the deep learning model input. Based on the experiment results, it is shown that the LSTM model is outperformed compared to the multi-layer NN model. The best accuracy performance provided by LSTM model is 91%, whereas the best accuracy performance of multi-layer NN model is 76%.

Recognizing emotions in restaurant online reviews: a hybrid model integrating deep learning and a sentiment lexicon

PurposeThis study aims to establish a model for rapid and accurate emotion recognition in restaurant online reviews, thus advancing the literature and providing practical insights into electronic word-of-mouth management for the industry.Design/methodology/approachThis study elaborates a hybrid model that integrates deep learning (DL) and a sentiment lexicon (SL) and compares it to five other models, including SL, random forest (RF), naïve Bayes, support vector machine (SVM) and a DL model, for the task of emotion recognition in restaurant online reviews. These models are trained and tested using 652,348 online reviews from 548 restaurants.FindingsThe hybrid approach performs well for valence-based emotion and discrete emotion recognition and is highly applicable for mining online reviews in a restaurant setting. The performances of SL and RF are inferior when it comes to recognizing discrete emotions. The DL method and SVM can perform satisfactorily in the valence-based emotion recognition.Research limitations/implicationsThese findings provide methodological and theoretical implications; thus, they advance the current state of knowledge on emotion recognition in restaurant online reviews. The results also provide practical insights into intelligent service quality monitoring and electronic word-of-mouth management for the industry.Originality/valueThis study proposes a superior model for emotion recognition in restaurant online reviews. The methodological framework and steps are elucidated in detail for future research and practical application. This study also details the performances of other commonly used models to support the selection of methods in research and practical applications.

Local food experiences before and after COVID-19: a sentiment analysis of EWOM

Purpose – To use Natural Language Processing (NLP) to explore how people feel and what they share online about their experiences with food. In addition, to learn how these experiences have evolved recently, differences before and during the crisis COVID -19 will be explored. Methodology/Design/Approach – A total of 35,001 reviews of restaurants and local cuisine establishments near tourist attractions in the city of Ayutthaya, Thailand, were extracted from the Google Local Guide platform. Several NLP techniques were used to analyse the text data, including sentiment analysis, word cloud analysis, and the N-gramme model. Findings – The results reveal travellers’ hidden sentiments toward dining experiences. Key attributes of experience sharing related to food activities in online reviews were identified both before and after COVID -19. From a theoretical perspective, the findings are relevant for researchers to recognise tourists’ behaviour in sharing local food experiences. From a practical perspective, decision makers will have a better understanding of tourist behaviour to develop and implement appropriate strategies. Originality of the research – This study is the first to analyse and interpret online reviews on Google Maps platform by applying text mining and sentiment analysis in gastronomic tourism research, especially in the context of COVID -19.