Convolutional neural encoding of online reviews for the identification of travel group type topics on TripAdvisor

Arenas‐Márquez, Francisco J.; Torres, María del Rocío Martínez; Toral, S. L.

doi:10.1016/j.ipm.2021.102645

Cited by 16 publications

(4 citation statements)

References 79 publications

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“…Regardless of the method used, many hospitality and tourism studies have applied CNNs to analyze texts from various perspectives because they can extract high-level semantic representations from review texts. For example, Arenas-Márquez et al (2021) extracted unique classes/topics associated with each traveler using a CNN to analyze traveler reviews. Moreover, Puh and Bagić Babac (2023), Liu and Zhao (2023) also applied CNN in sentiment analysis.…”

Section: Literature Review and Research Backgroundmentioning

confidence: 99%

A novel deep learning method to use feature complementarity for review helpfulness prediction

Li,

Jeong

et al. 2024

JHTT

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Purpose Most previous studies predicting review helpfulness ignored the significance of deep features embedded in review text and instead relied on hand-crafted features. Hand-crafted and deep features have the advantages of high interpretability and predictive accuracy. This study aims to propose a novel review helpfulness prediction model that uses deep learning (DL) techniques to consider the complementarity between hand-crafted and deep features. Design/methodology/approach First, an advanced convolutional neural network was applied to extract deep features from unstructured review text. Second, this study used previous studies to extract hand-crafted features that impact the helpfulness of reviews and enhance their interpretability. Third, this study incorporated deep and hand-crafted features into a review helpfulness prediction model and evaluated its performance using the Yelp.com data set. To measure the performance of the proposed model, this study used 2,417,796 restaurant reviews. Findings Extensive experiments confirmed that the proposed methodology performs better than traditional machine learning methods. Moreover, this study confirms through an empirical analysis that combining hand-crafted and deep features demonstrates better prediction performance. Originality/value To the best of the authors’ knowledge, this is one of the first studies to apply DL techniques and use structured and unstructured data to predict review helpfulness in the restaurant context. In addition, an advanced feature-fusion method was adopted to better use the extracted feature information and identify the complementarity between features.

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Section: Literature Review and Research Backgroundmentioning

confidence: 99%

A novel deep learning method to use feature complementarity for review helpfulness prediction

Li,

Jeong

et al. 2024

JHTT

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“…They used decision trees and fuzzy logic approaches for method development. Arenas-Márquez et al [ 34 ] focused on online review analysis to identify travel group-type topics. They collected data from TripAdvisor.…”

Section: Literature Reviewmentioning

confidence: 99%

Customer decision-making analysis based on big social data using machine learning: a case study of hotels in Mecca

Alsayat

2022

Neural Comput & Applic

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Big social data and user-generated content have emerged as important sources of timely and rich knowledge to detect customers’ behavioral patterns. Revealing customer satisfaction through the use of user-generated content has been a significant issue in business, especially in the tourism and hospitality context. There have been many studies on customer satisfaction that take quantitative survey approaches. However, revealing customer satisfaction using big social data in the form of eWOM (electronic word of mouth) can be an effective way to better understand customers’ demands. In this study, we aim to develop a hybrid methodology based on supervised learning, text mining, and segmentation machine learning approaches to analyze big social data on travelers’ decision-making regarding hotels in Mecca, Saudi Arabia. To do so, we use support vector regression with sequential minimal optimization (SMO), latent Dirichlet allocation (LDA), and k -means approaches to develop the hybrid method. We collect data from travelers’ online reviews of Mecca hotels on TripAdvisor. The data are segmented, and travelers’ satisfaction is revealed for each segment based on their online reviews of hotels. The results show that the method is effective for big social data analysis and traveler segmentation in Mecca hotels. The results are discussed, and several recommendations and strategies for hotel managers are provided to enhance their service quality and improve customer satisfaction.

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“…Li et al [38] perform text clustering using Sentence BERT as encoding of the text sentences, a weighting layer to increase the relevance of sentences as a function of the named entities contained, and K-means as the clustering algorithm. Arenas-Márquez et al [39] describes the use of a convolutional neural network to identify topics of interest in a collection of TripAdvisor messages using Word2Vec embeddings of the words in the documents as input. They compare this approach with respect to Latent Dirichlet Allocation encoding of the texts and Word2Vec mean.…”

Section: Related Workmentioning

confidence: 99%

Approaches for the Clustering of Geographic Metadata and the Automatic Detection of Quasi-Spatial Dataset Series

Lacasta

López-Pellicer

Zarazaga‐Soria

et al. 2022

IJGI

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The discrete representation of resources in geospatial catalogues affects their information retrieval performance. The performance could be improved by using automatically generated clusters of related resources, which we name quasi-spatial dataset series. This work evaluates whether a clustering process can create quasi-spatial dataset series using only textual information from metadata elements. We assess the combination of different kinds of text cleaning approaches, word and sentence-embeddings representations (Word2Vec, GloVe, FastText, ELMo, Sentence BERT, and Universal Sentence Encoder), and clustering techniques (K-Means, DBSCAN, OPTICS, and agglomerative clustering) for the task. The results demonstrate that combining word-embeddings representations with an agglomerative-based clustering creates better quasi-spatial dataset series than the other approaches. In addition, we have found that the ELMo representation with agglomerative clustering produces good results without any preprocessing step for text cleaning.

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Convolutional neural encoding of online reviews for the identification of travel group type topics on TripAdvisor

Cited by 16 publications

References 79 publications

A novel deep learning method to use feature complementarity for review helpfulness prediction

A novel deep learning method to use feature complementarity for review helpfulness prediction

Customer decision-making analysis based on big social data using machine learning: a case study of hotels in Mecca

Approaches for the Clustering of Geographic Metadata and the Automatic Detection of Quasi-Spatial Dataset Series

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