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The main goal of this study is to employ unsupervised (lexicon-based) learning approaches to identify readers' emotional dimensions and thumbs-up empathy reactions to reviews of online travel agency apps based on appraisal and stimulus–organism–response (SOR) theories.

Using the Google Play Scraper, we gathered a total of 402,431 reviews from the Google Play Store for two travel agency apps, Tripadvisor and Booking.com. Following the filtering and cleaning of user reviews, we used lexicon-based unsupervised machine learning algorithms to investigate the associations between various emotional dimensions of reviews and review readers' thumbs-up reactions.

The study's findings reveal that the sentiment of different sorts of reviews has a substantial influence on review readers' emotional experiences, causing them to give the app a thumbs up review. Furthermore, readers' thumbs-up responses to the text reviews differed depending on the eight emotional aspects of the reviews.

The results of this research can be applied in the development of online travel agency apps. The findings suggest that app developers can enhance users' emotional experiences by considering the sentiment and emotional aspects of reviews in their design and implementation. Additionally, the results can be used by travel agencies to improve their online reputation and attract more customers by providing a positive user experience.

The findings of this research have the potential to have a significant impact on society by providing insights into the emotional experiences of users when they engage with online travel agency apps. The study highlights the importance of considering the emotional aspect of user reviews, which can help app developers to create more user-friendly and empathetic products.

The current study is the first to evaluate the impact of users' thumbs-up empathetic reactions on user evaluations of online travel agency applications using unsupervised (lexicon-based) learning methodologies.

The influence of website quality on online compulsive buying behavior (OCBB) in the context of online shopping based on the usage of a credit card (UCC) and online impulsive buying behavior (OIBB) was investigated in this study.

The authors used a research model to examine the relationships between the study components as per the prescription. For this investigation, the authors used an online survey form to obtain primary data from 350 respondents on social media. A covariance-based structural equation modeling approach was used to evaluate the structural research model and data.

The findings reveal that the quality of online shopping websites positively affects consumers' UCC and OIBB, and these in turn positively influence their OCBB.

The study emphasized impacting elements on consumer behavior and gave advice for future research based on the results. Using several dimensions of website quality, this study bridges the knowledge gap between UCC, OIBB and OCBB.

Based on UCC and OIBB, the authors developed a new model to investigate the link between website quality and OCBB. To the best of the authors' knowledge, it is the first experimental result that assesses the impact of website quality on OCBB.

There is a strong prerequisite for organizations to analyze customer review behavior to evaluate the competitive business environment. The purpose of this study is to analyze and predict customer reviews of halal restaurants using machine learning (ML) approaches.

The authors collected customer review data from the Yelp website. The authors filtered the reviews of only halal restaurants from the original data set. Following cleaning, the filtered review texts were classified as positive, neutral or negative sentiments, and those sentiments were scored using the AFINN and VADER sentiment algorithms. Also, the current study applies four machine learning methods to classify each review toward halal restaurants into its sentiment class.

The experiment showed that most of the customer reviews toward halal restaurants were positive. The authors also discovered that all of the methods (decision tree, linear support vector machine, logistic regression and random forest classifier) can correctly classify the review text into sentiment class, but logistic regression outperforms the others in terms of accuracy.

The results facilitate halal restaurateurs in identifying customer review behavior.

Sentiment and emotions, according to appraisal theory, form the basis for all interactions, facilitating cognitive functions and supporting prospective customers in making sense of experiences. Emotion theory also describes human affective states that determine motives and actions. The study looks at how potential customers might react to a halal restaurant’s consensus on social media based on reviewers’ opinions of halal restaurants because emotions can be conveyed through reviews.

This study applies machine learning approaches to analyze and predict customer sentiment based on the review texts toward halal restaurants.

Social commerce is a subpart of electronic commerce (e-commerce), where social media is forced to support user contributions. The purpose of this study is to measure the impact of customers' interpersonal interactions in social commerce on customer relationship management (CRM) performance, based on the flow, commitment-trust and stimulus–organism–response (SOR) theories.

On the basis of the SOR framework, the authors developed a study model to determine the impact on CRM performance of customers' interpersonal interactions in social commerce. The primary data of the study were collected from 640 users of social commerce through a web questionnaire during the COVID-19 (coronavirus disease 2019) pandemic situation, and the authors tested the study model using the approach of covariance-based structural equation modeling (SEM).

Results of the current study reveal that customers' interpersonal interactions in social commerce optimistically influence their perceived flow. Moreover, perceived flow absolutely controls users' trust and CRM performance. In turn, collective users' trust positively influences users' commitment and CRM performance. Finally, collective users' commitment absolutely influences the performance of CRM.

The authors provide a valuable contribution to the theoretical field of online marketing and CRM. Besides, the findings of this study are relevant for marketers to know the issues for increasing customer trust, commitment and performance of CRM.

The current study develops a model based on the flow, commitment-trust and stimulus–organism–response (SOR) theories. The authors' research is the first to estimate the effect of customers' interpersonal interactions in social commerce on CRM performance.