Application of Classification and Word Embedding Techniques to Evaluate Tourists’ Hotel-revisit Intention

Christodoulou, Evripides; Gregoriades, Andreas; Pampaka, María; Herodotou, Herodotos

doi:10.5220/0010453502160223

Cited by 5 publications

(3 citation statements)

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“…The study done by Christodoulou et al [12] solved the problem of revisiting tourists from the point of view of big data analysis. The applied method used topic modeling, word embedding, XGBoost, and random forest classification algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of the Customer Churn Prediction Project in the Hotel Industry Based on Text Mining and the Random Forest Algorithm

Taherkhani,

Daneshvar,

Amoozad Khalili

et al. 2023

Advances in Civil Engineering

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The ability of hotels to differentiate themselves from competitors and continue to operate profitably depends on their ability to retain their customers by building long-term and permanent customer relationships. Technological developments in recent years have made it possible for companies to predict their customers’ behavior by accessing their opinions faster and preventing them from churning. Managing customer churn prediction projects has become an important issue today, especially in the hotel industry. Therefore, this research seeks to analyze projects that predict the churn of hotel customers to provide a model to help hotel managers in this field. In this research, an approach based on text mining on customers’ comments in the Persian language is presented, which uses the random forest algorithm for classification that was considered the most effective method to solve this problem. In this model, to increase the efficiency of the proposed method in compare with existing works, the gravitational search algorithm was used to select the useful features, and the differential evolution algorithm was used to adjust the parameters of the classification method. The dataset of this research is the collected data from the customer database on social networks and hotels’ websites, especially the hotels on Kish Island in Iran. The results of this research showed that after the implementation of the preprocessing operations, the method of adjusting the parameters and removing the unimportant features, the model’s accuracy increased significantly. The precision, recall, F1, and accuracy criteria were 0.77, 0.76, 0.76, and 0.77, respectively.

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Section: Literature Reviewmentioning

confidence: 99%

Analysis of the Customer Churn Prediction Project in the Hotel Industry Based on Text Mining and the Random Forest Algorithm

Taherkhani,

Daneshvar,

Amoozad Khalili

et al. 2023

Advances in Civil Engineering

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“…However, such a revisitintention dataset is not available, hence, a sentiment-labelled dataset with hotel reviews was used, acting as a proxy for revisit intention given that satisfaction is a prerequisite of revisit intention. The utilization of such labelled data is motivated by our previous work in revisit intention [96] and other similar research literature that uses sentiment as a proxy for revisit [26]. The rationale is that tourists who provide extremely positive reviews will possibly want to revisit a hotel and thus their eWOM might include words pointing to that intention.…”

Section: Intention Filteringmentioning

confidence: 99%

Explaining tourist revisit intention using natural language processing and classification techniques

et al. 2023

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Revisit intention is a key indicator of business performance, studied in many fields including hospitality. This work employs big data analytics to investigate revisit intention patterns from tourists’ electronic word of mouth (eWOM) using text classification, negation detection, and topic modelling. The method is applied on publicly available hotel reviews that are labelled automatically based on consumers’ intention to revisit a hotel or not. Topics discussed in revisit-annotated reviews are automatically extracted and used as features during the training of two Extreme Gradient Boosting models (XGBoost), one for each of two hotel categories (2/3 and 4/5 stars). The emerging patterns from the trained XGBoost models are identified using an explainable machine learning technique, namely SHAP (SHapley Additive exPlanations). Results show how topics discussed by tourists in reviews relate with revisit/non revisit intention. The proposed method can help hoteliers make more informed decisions on how to improve their services and thus increase customer revisit occurrences.

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“…Word embedding is a continuous vector representation of words that encodes the meaning of the word, such that the words that are closer in the vector space are supposed to be similar in the meaning. The use of word embeddings as additional features improves the performance in many NLP tasks, including text classification [22][23][24][25][26][27][28][29][30]. Different Machine Learning algorithms can be trained to derive these vectors, such as Word2Vec [31], FastText [32], Glove [33].…”

Section: Literature Reviewmentioning

confidence: 99%

Supervised and Unsupervised Categorization of an Imbalanced Italian Crime News Dataset

Rollo

Bonisoli

2022

Lecture Notes in Business Information Processing

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The automatic categorization of crime news is useful to create statistics on the type of crimes occurring in a certain area. This assignment can be treated as a text categorization problem. Several studies have shown that the use of word embeddings improves outcomes in many Natural Language Processing (NLP), including text categorization. The scope of this paper is to explore the use of word embeddings for Italian crime news text categorization. The approach followed is to compare different document pre-processing, Word2Vec models and methods to obtain word embeddings, including the extraction of bigrams and keyphrases. Then, supervised and unsupervised Machine Learning categorization algorithms have been applied and compared. In addition, the imbalance issue of the input dataset has been addressed by using Synthetic Minority Oversampling Technique (SMOTE) to oversample the elements in the minority classes. Experiments conducted on an Italian dataset of 17,500 crime news articles collected from 2011 till 2021 show very promising results. The supervised categorization has proven to be better than the unsupervised categorization, overcoming 80% both in precision and recall, reaching an accuracy of 0.86. Furthermore, lemmatization, bigrams and keyphrase extraction are not so decisive. In the end, the availability of our model on GitHub together with the code we used to extract word embeddings allows replicating our approach to other corpus either in Italian or other languages.

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Application of Classification and Word Embedding Techniques to Evaluate Tourists’ Hotel-revisit Intention

Cited by 5 publications

References 0 publications

Analysis of the Customer Churn Prediction Project in the Hotel Industry Based on Text Mining and the Random Forest Algorithm

Analysis of the Customer Churn Prediction Project in the Hotel Industry Based on Text Mining and the Random Forest Algorithm

Explaining tourist revisit intention using natural language processing and classification techniques

Supervised and Unsupervised Categorization of an Imbalanced Italian Crime News Dataset

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