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Customers generally give ratings and reviews for different services that they get online or offline. These reviews and ratings aspects are effectively helpful to both the company and customers to receive feedback and make the right decisions, respectively. However, the number of reviews and ratings can increase exponentially, bringing a new challenge for the company to manage and track. Under these circumstances, it will also be hard for the customer to make the right decision. In this work, we summarize text reviews and ratings given by passengers for different airlines. The objective of this research is to predict whether the recommendation made by the customer is positive or negative. Two types of features, namely, textual feature and explicit ratings, are extracted from the dataset and other attributes. We found the relationship between such sentiments and feelings expressed in online reviews and predictive consumer recommendation decisions. We have considered quantitative content with qualitative content of online reviews in predicting recommendation decisions, which shows the work's novelty. Additionally, the obtained results yield an essential contribution to the existing literature in terms of service evaluation, making managerial policies, and predictive consumer recommendations, etc. Moreover, we hope that this work would be helpful for practitioners who wish to utilize the technique to make the quick and essential hidden information by combining textual reviews and various service aspects ratings. [ABSTRACT FROM AUTHOR]

Consumer sentiment analysis is a recent fad for social media-related applications such as healthcare, crime, finance, travel, and in academia. Disentangling consumer perception to gain insight into the desired objective and reviews is significant. With the advancement of technology, a massive amount of social web data increasing in volume, subjectivity, and heterogeneity becomes challenging to process manually. Machine learning (ML) techniques have been utilized to handle this difficulty in real-life applications. This paper presents a study to determine the usefulness, scope, and applicability of this alliance of ML techniques for consumer sentiment analysis (CSA) for online reviews in the domain of hospitality and tourism. We show a systematic literature review to compare, analyse, explore, and understand the attempts and directions to find research gaps in illustrating the future scope of this pairing. The primary objective is to read and analyse the use of ML techniques for consumer sentiment analysis on online reviews in the domain of hospitality and tourism. This research has significant implications for service providers in terms of developing managerial strategies for consumers in terms of selecting services that meet their needs. Furthermore, there is high impact for researchers in terms of prospective research directions. [ABSTRACT FROM AUTHOR]

Digital technology and social media have delivered many advantages in understanding human psychology, which is essential to industrial growth. Skytrax is an online social media platform for travellers to write reviews on airlines. In online reviews, consumer recommendations are critical indicators for service providers to improve their consumer policies as well as service quality. It is also helpful for future customers to help get information on future purchases prior to making them. Therefore, previous consumer recommendations based on online reviews play a vital role in airline recommendations. Our main goal is to use our proposed cuckoo optimized machine learning model to predict airline recommendations. Experimental analysis was implemented using data scraped from the website https://www.airlinequality.com. Our results show that the proposed eXtreme gradient boosting classifier optimized by Cuckoo Search (CS-XGB) outperforms other state-of-the-art techniques. [Display omitted] • Propose a consumer recommendation prediction system using online review. • Acquired consumer online reviews and ratings from Skytrax. • Applied various pre-processing schemes to input data and feed it to the model. • Create a Cuckoo Search (CS) based parameter optimization model. • Proposed CS-XGB outperforms the other state-of-the-art techniques. [ABSTRACT FROM AUTHOR]