Between comments and repeat visit: capturing repeat visitors with a hybrid approach

Kim, Jina; Jang, Yeonju; Bae, Kunwoo; Oh, Soyoung; Jeong, Nam Jeong; Park, Eunil; Han, Jinyoung; Pobil, Ángel P. del

doi:10.1108/dta-06-2020-0123

Cited by 5 publications

(3 citation statements)

References 44 publications

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“…Analyzing reviews using machine learning to develop the locations has been done before by other researchers using latent Dirichlet allocation and NB algorithms (Taecharungroj and Mathayomchan, 2019). Kim et al (2021) tried to use a hybrid approach to understand the customers' revisiting behavior to a hotel; they used two types of approaches and compared the accuracy of customers' post-visit behavior. In one method, they applied machine learning to predict whether customers who have written the reviews would revisit the hotel or not; the second method was divided into two parts; in the first part (prediction part), they made some participants read the reviews and asked them to predict whether the customers would revisit the hotel or not; in the second part (decision part), the participants were also asked whether they would visit the hotels after reading the reviews written by the customers.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of polarities of online hotel reviews: an improved stacked decision tree (ISD) approach

Trivedi

Singh²,

Malhotra

2022

GKMC

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Purpose There is a need to predict whether the consumers liked the stay in the hotel rooms or not, and to remove the aspects the customers did not like. Many customers leave a review after staying in the hotel. These reviews are mostly given on the website used to book the hotel. These reviews can be considered as a valuable data, which can be analyzed to provide better services in the hotels. The purpose of this study is to use machine learning techniques for analyzing the given data to determine different sentiment polarities of the consumers. Design/methodology/approach Reviews given by hotel customers on the Tripadvisor website, which were made available publicly by Kaggle. Out of 10,000 reviews in the data, a sample of 3,000 negative polarity reviews (customers with bad experiences) in the hotel and 3,000 positive polarity reviews (customers with good experiences) in the hotel is taken to prepare data set. The two-stage feature selection was applied, which first involved greedy selection method and then wrapper method to generate 37 most relevant features. An improved stacked decision tree (ISD) classifier) is built, which is further compared with state-of-the-art machine learning algorithms. All the tests are done using R-Studio. Findings The results showed that the new model was satisfactory overall with 80.77% accuracy after doing in-depth study with 50–50 split, 80.74% accuracy for 66–34 split and 80.25% accuracy for 80–20 split, when predicting the nature of the customers’ experience in the hotel, i.e. whether they are positive or negative. Research limitations/implications The implication of this research is to provide a showcase of how we can predict the polarity of potentially popular reviews. This helps the authors’ perspective to help the hotel industries to take corrective measures for the betterment of business and to promote useful positive reviews. This study also has some limitations like only English reviews are considered. This study was restricted to the data from trip-adviser website; however, a new data may be generated to test the credibility of the model. Only aspect-based sentiment classification is considered in this study. Originality/value Stacking machine learning techniques have been proposed. At first, state-of-the-art classifiers are tested on the given data, and then, three best performing classifiers (decision tree C5.0, random forest and support vector machine) are taken to build stack and to create ISD classifier.

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Section: Literature Reviewmentioning

confidence: 99%

Prediction of polarities of online hotel reviews: an improved stacked decision tree (ISD) approach

Trivedi

Singh²,

Malhotra

2022

GKMC

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“…Sentiment analysis is gaining popularity both in academia and industry. Corporations analyze the sentiments on social media platforms posted by consumers such as Facebook, Instagram and Twitter, and other online reviews and utilize them for corporate management, such as strategic planning, decision-making and customer relationship management (Capuano et al ., 2021; Kim et al , 2021b). Conversely, sentiment analysis can be used to better understand a company.…”

Section: Literature Reviewmentioning

confidence: 99%

Do SEC filings indicate any trends? Evidence from the sentiment distribution of forms 10-K and 10-Q with FinBERT

Kim

Lee

Yoo

2023

DTA

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PurposeThis study quantified companies' views on the COVID-19 pandemic with sentiment analysis of US public companies' disclosures. The study aims to provide timely insights to shareholders, investors and consumers by exploring sentiment trends and changes in the industry and the relationship with stock price indices.Design/methodology/approachFrom more than 50,000 Form 10-K and Form 10-Q published between 2020 and 2021, over one million texts related to the COVID-19 pandemic were extracted. Applying the FinBERT fine-tuned for this study, the texts were classified into positive, negative and neutral sentiments. The correlations between sentiment trends, differences in sentiment distribution by industry and stock price indices were investigated by statistically testing the changes and distribution of quantified sentiments.FindingsFirst, there were quantitative changes in texts related to the COVID-19 pandemic in the US companies' disclosures. In addition, the changes in the trend of positive and negative sentiments were found. Second, industry patterns of positive and negative sentiment changes were similar, but no similarities were found in neutral sentiments. Third, in analyzing the relationship between the representative US stock indices and the sentiment trends, the results indicated a positive relationship with positive sentiments and a negative relationship with negative sentiments.Originality/valuePerforming sentiment analysis on formal documents like Securities and Exchange Commission (SEC) filings, this study was differentiated from previous studies by revealing the quantitative changes of sentiment implied in the documents and the trend over time. Moreover, an appropriate data preprocessing procedure and analysis method were presented for the time-series analysis of the SEC filings.

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“…Online consumer review (OCR) is also known as electronic word of mouth (eWOM). With the rapid development of the Internet, how to accurately obtain competitive intelligence through online reviews has become increasingly important, as consumers usually read eWOM of some alternative products when they shop online and seek the products to their best satisfaction through comparison (Kim et al , 2021). As open information resources, OCRs have gradually drawn the attention of e-commerce enterprises, manufacturers and competitors.…”

Section: Introductionmentioning

confidence: 99%

Product competitiveness analysis from the perspective of customer perceived helpfulness: a novel method of information fusion research

Wang

Zhang

et al. 2022

DTA

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PurposeWith the continuous development of online shopping, analyzing the competitiveness of products in the fierce market competition is becoming increasingly crucial to position their own product development. However, the information overload brought by the network development makes it getting difficult to obtain the accurate competitiveness information. Therefore, competitiveness analysis research to combine with the perceived helpfulness study needs urgent solution. Furthermore, deviations exist in the three common methods of perceived helpfulness research. Finally, the traditional information fusion analysis only analyzes the advantages and disadvantages of products in competitiveness analysis without taking account of the competitive environment.Design/methodology/approachThis study puts forward a novel prediction model of perceived helpfulness in conjunction of unsupervised learning and sentiment analysis techniques, to conduct the comparison with pros and cons of congeneric products.FindingsThis paper adopts Wilcoxon test to demonstrate the significant rectification of our competitiveness analysis to the traditional methods. It is noted that the positive reviews of the products in this study impact more on product word of mouth and competitiveness than negative ones.Originality/valueTo sum up, the results of this study benefit businesses in locating their dynamic market position with competitors in practice and exploring new method for long-term development strategic planning.

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Between comments and repeat visit: capturing repeat visitors with a hybrid approach

Cited by 5 publications

References 44 publications

Prediction of polarities of online hotel reviews: an improved stacked decision tree (ISD) approach

Prediction of polarities of online hotel reviews: an improved stacked decision tree (ISD) approach

Do SEC filings indicate any trends? Evidence from the sentiment distribution of forms 10-K and 10-Q with FinBERT

Product competitiveness analysis from the perspective of customer perceived helpfulness: a novel method of information fusion research

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