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The purpose of this study is to develop a review rating prediction method based on a supervised text mining approach for unrated customer reviews.

Using 2,851 hotel comment card (HCC) reviews, this paper manually labeled positive and negative comments with seven aspects (dining, cleanliness, service, entertainment, price, public, room) that emerged from the content of said reviews. After text preprocessing (tokenization, eliminating punctuation, stemming, etc.), two classifier models were created for predicting the reviews’ sentiments and aspects. Thus, an aggregate rating scale was generated using these two classifier models to determine overall rating values.

A new algorithm, Comment Rate (CRate), based on supervised learning, is proposed. The results are compared with another review-rating algorithm called location based social matrix factorization (LBSMF) to check the consistency of the proposed algorithm. It is seen that the proposed algorithm can predict the sentiments better than LBSMF. The performance evaluation is performed on a real data set, and the results indicate that the CRate algorithm truly predicts the overall rating with ratio 80.27%. In addition, the CRate algorithm can generate an overall rating prediction scale for hotel management to automatically analyze customer reviews and understand the sentiment thereof.

The review data were only collected from a resort hotel during a limited period. Therefore, this paper cannot explore the effect of independent variables on the dependent variable in context of larger period.

This paper provides a novel overall rating prediction technique allowing hotel management to improve their operations. With this feature, hotel management can evaluate guest feedback through HCCs more effectively and quickly. In this way, the hotel management will be able to identify those service areas that need to be developed faster and more effectively. In addition, this review rating prediction approach can be applied to customer reviews posted via online platforms for detecting review and rating reliability.

Manually analyzing textual information is time-consuming and can lead to measurement errors. Therefore, the primary contribution of this study is that although comment cards do not have rating values, the proposed CRate algorithm can predict the overall rating and understand the sentiment of the reviews in question.

To test if the factors “reviewer location” and “time frame” have any impact on the prediction results when predicting online product ratings from user reviews.

Reviews and ratings are scraped for the product “The Secret” book through Web pages of e-commerce websites like Amazon and Flipkart. Such data is used for training the model to predict ratings of similar products based on reviews data in various other social media platforms like Facebook, Quora and YouTube. After data preprocessing, sentiment analysis is used for opinion classification. A multi-class supervised support vector machine is used for feature classification and predictions. The four models produced in the study have a prediction accuracy of 79%. The data collection is done based on a specific geographical location and specific time frame. Post evaluating the predictions, inferential statistics are used to check for significance.

There will be an impact on the ratings predicted from the reviews that belong to a particular geographic location or time frame. The ratings predicted from such reviews help in taking accurate decisions as they are robust and informative.

This study is confined to a single product and for cross domain social media pages, only Facebook, YouTube and Quora data are considered.

Provides credible ratings of a product/service on all cross domain social media pages making the initial screening process of purchase decisions better.

Many studies explored the usefulness of reviews for rating prediction based on review nature. This study aims to identify the usefulness of reviews based on factors that would reduce uncertainty in the purchase process.

This paper aims to develop a model to predict online review ratings from multiple sources, which can be used to detect fraudulent reviews and create proprietary rating indexes, or which can be used as a measure of selection in recommender systems.

This study applies machine learning and natural language processing approaches to combine features derived from the qualitative component of a review with the corresponding quantitative component and, therefore, generate a richer review rating.

Experiments were performed over a collection of hotel online reviews – written in English, Spanish and Portuguese – which shows a significant improvement over the previously reported results, and it not only demonstrates the scientific value of the approach but also strengthens the value of review prediction applications in the business environment.

This study shows the importance of building predictive models for revenue management and the application of the index generated by the model. It also demonstrates that, although difficult and challenging, it is possible to achieve valuable results in the application of text analysis across multiple languages.

The purpose of this paper is to assess the reliability of numerical ratings of hotels calculated by three sentiment analysis algorithms.

More than one million reviews and numerical ratings of hotels in seven cities in four countries were extracted from TripAdvisor web site. Reviews were classified as positive or negative using three sentiment analysis tools. The percentage of positive reviews was used to predict numerical ratings that were then compared with actual ratings.

All tools classified reviews as positive or negative in a way that correlated positively with numerical ratings. More complex algorithms worked better, yet predicted ratings showed reasonable agreement with actual ratings for most cities. Predictions for hotels were less reliable if based on less than 50-60 percent of available reviews.

These results validate that sentiment analysis can be used to transform unstructured qualitative data on user opinion into quantitative ratings. Current tools may be useful for summarizing opinions of user reviews of products and services on web sites that do not require users to post numerical ratings such as traveler forums. This summarizing may be valuable not just to potential users, but also to the service and product providers and offers validation and benchmarking for future improvement of opinion mining and prediction techniques.

This work assesses the correlation between sentiment analysis of hotels’ reviews and their actual ratings. The authors also evaluated the reliability of results of sentiment analysis calculated by three different algorithms.

The purpose of this chapter is to shed light on researchers and practitioners about sentiment analysis in hospitality and tourism. The technical details described throughout the chapter with a case study to provide clarifying insights. The proposed chapter adds significantly to the body of text mining knowledge by combining a technical explanation with a relevant case study. The case study used supervised machine learning to predict overall star ratings based on 20,247 comments related to Royal Caribbean International services for determining the impact of cruise travel experiences on the evaluation company process. The results indicate that travelers evaluate their travel experiences according to the most intense negative or positive feelings they have about the company.

Prior literature on online customer reviews (OCRs) suggests that individuals are socially influenced by information shared by others. Given that the online environment brings together users from different cultures, understanding how users differ in their processing and generation of OCRs across cultures is imperative. Specifically, this paper explores how cross-cultural differences influence OCR generation when there are inconsistencies between recent and overall review ratings.

The authors employ an empirical study and an experimental approach to test the predictions. For the empirical study (Study 1), the authors collected and analyzed actual review data from an online hotel review platform, Booking.com. This was followed by an experimental study (Study 2) in which the authors manipulated the thinking style represented by each cultural orientation to further explain how and why cross-cultural differences exist.

The results show that compared with the review ratings of users from collectivist cultures, those of users from individualistic cultures are more likely to follow recent review ratings. Based on the experimental study, the authors further find that such cross-cultural differences in OCR generation are driven by differences in thinking style.

This research extends the literature by demonstrating the cross-cultural differences in individuals' herding tendencies in OCR generation. The authors also add to the literature by showing in which direction OCR herding occurs when there is a discrepancy between overall and recent review ratings. From a managerial perspective, the findings provide guidelines for online platforms serving the global market on predicting customers' OCR generation and constructing appropriate response strategies.

The widespread acceptance of various social platforms has increased the number of users posting about various services based on their experiences about the services. Finding out the intended ratings of social media (SM) posts is important for both organizations and prospective users since these posts can help in capturing the user’s perspectives. However, unlike merchant websites, the SM posts related to the service-experience cannot be rated unless explicitly mentioned in the comments. Additionally, predicting ratings can also help to build a database using recent comments for testing recommender algorithms in various scenarios.

In this study, the authors have predicted the ratings of SM posts using linear (Naïve Bayes, max-entropy) and non-linear (k-nearest neighbor, k-NN) classifiers utilizing combinations of different features, sentiment scores and emotion scores.

Overall, the results of this study reveal that the non-linear classifier (k-NN classifier) performed better than the linear classifiers (Naïve Bayes, Max-entropy classifier). Results also show an improvement of performance where the classifier was combined with sentiment and emotion scores. Introduction of the feature “factors of importance” or “the latent factors” also show an improvement of the classifier performance.

This study provides a new avenue of predicting ratings of SM feeds by the use of machine learning algorithms along with a combination of different features like emotional aspects and latent factors.

As the tourism industry becomes more vital for the success of many economies around the world, the importance of technology in tourism grows daily. Alongside increasing tourism importance and popularity, the amount of significant data grows, too. On daily basis, millions of people write their opinions, suggestions and views about accommodation, services, and much more on various websites. Well-processed and filtered data can provide a lot of useful information that can be used for making tourists' experiences much better and help us decide when selecting a hotel or a restaurant. Thus, the purpose of this study is to explore machine and deep learning models for predicting sentiment and rating from tourist reviews.

This paper used machine learning models such as Naïve Bayes, support vector machines (SVM), convolutional neural network (CNN), long short-term memory (LSTM) and bidirectional long short-term memory (BiLSTM) for extracting sentiment and ratings from tourist reviews. These models were trained to classify reviews into positive, negative, or neutral sentiment, and into one to five grades or stars. Data used for training the models were gathered from TripAdvisor, the world's largest travel platform. The models based on multinomial Naïve Bayes (MNB) and SVM were trained using the term frequency-inverse document frequency (TF-IDF) for word representations while deep learning models were trained using global vectors (GloVe) for word representation. The results from testing these models are presented, compared and discussed.

The performance of machine and learning models achieved high accuracy in predicting positive, negative, or neutral sentiments and ratings from tourist reviews. The optimal model architecture for both classification tasks was a deep learning model based on BiLSTM. The study’s results confirmed that deep learning models are more efficient and accurate than machine learning algorithms.

The proposed models allow for forecasting the number of tourist arrivals and expenditure, gaining insights into the tourists' profiles, improving overall customer experience, and upgrading marketing strategies. Different service sectors can use the implemented models to get insights into customer satisfaction with the products and services as well as to predict the opinions given a particular context.

This study developed and compared different machine learning models for classifying customer reviews as positive, negative, or neutral, as well as predicting ratings with one to five stars based on a TripAdvisor hotel reviews dataset that contains 20,491 unique hotel reviews.

This study aims to investigate the current state of research using deep learning methods for text classification in the tourism and hospitality field and to propose specific guidelines for future research.

This study undertakes a qualitative and critical review of studies that use deep learning methods for text classification in research fields of tourism and hospitality and computer science. The data was collected from the Web of Science database and included studies published until February 2022.

Findings show that current research has mainly focused on text feature classification, text rating classification and text sentiment classification. Most of the deep learning methods used are relatively old, proposed in the 20th century, including feed-forward neural networks and artificial neural networks, among others. Deep learning algorithms proposed in recent years in the field of computer science with better classification performance have not been introduced to tourism and hospitality for large-scale dissemination and use. In addition, most of the data the studies used were from publicly available rating data sets; only two studies manually annotated data collected from online tourism websites.

The applications of deep learning algorithms and data in the tourism and hospitality field are discussed, laying the foundation for future text mining research. The findings also hold implications for managers regarding the use of deep learning in tourism and hospitality. Researchers and practitioners can use methodological frameworks and recommendations proposed in this study to perform more effective classifications such as for quality assessment or service feature extraction purposes.

The paper provides an integrative review of research in text classification using deep learning methods in the tourism and hospitality field, points out newer deep learning methods that are suitable for classification and identifies how to develop different annotated data sets applicable to the field. Furthermore, foundations and directions for future text classification research are set.

Two valuable pieces of information – reviews and their corresponding numerical ratings – are accessible to potential customers before they make a purchasing decision. An extensive body of marketing literature has scrutinized the influence of customers’ reviews by linking such aspects as the volume and valance of reviews with product sales and customers’ purchase intention. The aim of this study, for which dual coding theory was used, was to understand the relationship between reviews and their corresponding numerical ratings.

The authors used the latent Dirichlet allocation technique to categorize customers’ reviews. The present findings contribute to the literature by showing the underlying mechanisms that customers use to interpret reviews and associate them with numerical ratings.

The gradient boosted decision tree model demonstrates that non-abstract-dominant reviews (reviews mainly consist of tangible objects, actions, events or affective words) are significant predictors of their corresponding numerical ratings. However, abstract-dominant reviews (i.e. those consisting primarily of intangible objects, events or actions) cannot predict their associated numerical ratings.

The present findings contribute to the literature by showing the underlying mechanisms that customers use to interpret reviews and associate them with numerical ratings.