Listening to the voice of the guest: A framework to improve decision-making processes with text data

Marcolin, Carla Bonato; Becker, João Luiz; Wild, Fridolin; Behr, Ariel

doi:10.1016/j.ijhm.2020.102853

Cited by 15 publications

(15 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The following two studies are representative of the former. Marcolin et al (2021) used the NLP technique based on deep learning to study 2,582 manually labeled hotel online reviews to analyze service quality by textual features. In this model, neural network is used to find patterns from unstructured text and feed-forward neural networks that are combined to represent the nonlinear relationships between the input and output layers (category probability of service quality).…”

Section: Text Feature Classificationmentioning

confidence: 99%

“…Text classification is an important research area in tourism and hospitality. Classifying user-generated content not only gives access to text features but also to consumer sentiment and rating, which helps researchers and tourism managers understand consumer behavior and assess tourist quality and satisfaction (Marcolin et al , 2021; Liu et al , 2022a; Ray et al , 2021). Text classification is the process of sorting text into different categories according to certain rules.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Text classification in tourism and hospitality – a deep learning perspective

Mehraliyev

et al. 2023

IJCHM

View full text Add to dashboard Cite

Purpose 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. Design/methodology/approach 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 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. Practical implications 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. Originality/value 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.

show abstract

Section: Text Feature Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Text classification in tourism and hospitality – a deep learning perspective

Mehraliyev

et al. 2023

IJCHM

View full text Add to dashboard Cite

show abstract

“…The research on competition analysis mainly involves the following problems: key customer requirement mining (Albayrak et al , 2021; Gang and Chenglin, 2021), market structure analysis (Netzer et al , 2012), competitors and competitive groups identification (Hu and Trivedi, 2020; Liu et al , 2019; Marcolin et al , 2021), main competitive advantages and disadvantages analysis (He et al , 2015; Jin et al ., 2016a; Kim and Kang, 2018; Wang et al , 2018a), how to improve the performance level of products or services in the competitive environment (Rodríguez-Díaz and Espino-Rodríguez, 2018). In order to identify competitors, Gao et al (2018) proposed a new model to extract comparison relationships from online reviews and constructed three types of comparison relationship networks.…”

Section: Research On Ewom For Product and Service Quality Improvementmentioning

confidence: 99%

Research on electronic word-of-mouth for product and service quality improvement: bibliometric analysis and future directions

Wang

Meng²,

Xu³

et al. 2022

IJICC

View full text Add to dashboard Cite

PurposeThis paper aims to analyze high-quality papers on the research of electronic word-of-mouth (eWOM) for product and service quality improvement from 2009 to 2022, in order to fully understand their historical progress, current situation and future development trend.Design/Methodology/ApproachThis paper adopts the bibliometrics method to analyze the relevant literature, including publishing trend and citation status, regional and discipline area distribution, and influential publications. Secondly, the VOSviewer is used for literature co-citation analysis and keyword co-occurrence analysis to obtain the basic literature and research hotspots in this research field.FindingsFirstly, the study finds that the number of publications basically shows an increasing trend, and those publications are mainly published in tourism journals. In addition, among these papers, China has the largest number of publications, followed by the USA and South Korea. Through co-citation analysis of literature and keyword co-occurrence analysis, 22 foundational papers and six main research topics are obtained in this paper. Finally, this paper elaborates on the development trend of the research topic and future research directions in detail.Originality/valueThis is the first paper that uses bibliometrics to analyze and review relevant researches on eWOM for product and service quality improvement, which is helpful for researchers to quickly understand its development status and trend. This review also provides some future research directions and provides a reference for further research.

show abstract

“…As organizações buscam adotar tecnologias como machine learning, deep learning e machine reasoning, incorporando recursos e funções como reconhecimento de padrões, transcrição de voz, visão computacional, modelos preditivos e prescritivos, robôs inteligentes, automação de processos e sistemas de suporte à decisão 14 , 25 . Ao fazê-lo, submetem-se aos riscos inerentes à IA.…”

Section: Framework Para a Gestão De Riscos De Inteligência Artificialunclassified