Measuring Hotel Service Quality in Borobudur Temple Using Opinion Mining

Rus, Annisa Marlin Masbar; Annisa, Rossi; Surjandari, Isti; Zulkarnain, Zulkarnain

doi:10.1109/icsssm.2019.8887650

Cited by 6 publications

(8 citation statements)

References 6 publications

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“…To get data for the sentiment analysis, the reviews are extracted by web scraping and transformed into a machine-readable format for use during the classification process [27] [28] [29].Thus, it ensures only critical parts of the text are kept, avoiding noisy data, such as the computational time, and ensuring the accuracy of the classifiers [30] [31]. Tokenization is used to break text documents into meaningful elements, and tokens are used in the next stage [23] [25] [29] [32] [33] [34].…”

Section: Sentiment Analysismentioning

confidence: 99%

“…Tokenization is used to break text documents into meaningful elements, and tokens are used in the next stage [23] [25] [29] [32] [33] [34]. The word separator characters, such as spaces, are identified and used as delimiters for the tokens [23] [29]. N-gram tokenization, such as unigram, bigram, and trigram, is applied to tokenize the reviews into terms [18] [35] [36] Any Stop Word (words that often appear in a text but have no meaning) is discarded, and the Case folding task converts all the upper-case letters in the text into lower cases to uniformize the shape of the letters [29] [35] [37].…”

Section: Sentiment Analysismentioning

confidence: 99%

“…The word separator characters, such as spaces, are identified and used as delimiters for the tokens [23] [29]. N-gram tokenization, such as unigram, bigram, and trigram, is applied to tokenize the reviews into terms [18] [35] [36] Any Stop Word (words that often appear in a text but have no meaning) is discarded, and the Case folding task converts all the upper-case letters in the text into lower cases to uniformize the shape of the letters [29] [35] [37]. The Word Normalization technique consists of Stemming and Lemmatization, which can be applied to eliminate the word's prefix and suffix based on the root word [23]…”

Section: Sentiment Analysismentioning

confidence: 99%

“…Meanwhile, Term weighting is used to find the repetitive terms in a corpus to determine their significance [29] [32] [39]. A term is a word or phrase in a document used to understand the context of the whole corpus that has a weight associated with it.…”

Section: Sentiment Analysismentioning

confidence: 99%

See 3 more Smart Citations

Sentiment classification from reviews for tourism analytics

Haris

Mutalib

Malik

et al. 2023

Int. J. Adv. Intell. Informatics

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User-generated content is critical for tourism destination management as it could help them identify their customers' opinions and come up with solutions to upgrade their tourism organizations as it could help them identify customer opinions. There are many reviews on social media and it is difficult for these organizations to analyse the reviews manually. By applying sentiment classification, reviews can be classified into several classes and help ease decision-making. The reviews contain noisy contents, such as typos and emoticons, which could affect the accuracy of the classifiers. This study evaluates the reviews using Support Vector Machine and Random Forest models to identify a suitable classifier. The main phases in this study are data collection, data preparation, data labelling and modelling phases. The reviews are labelled into three sentiments; positive, neutral, and negative. During pre-processing, steps such as removing the missing value, tokenization, case folding, stop words removal, stemming, and applying n-grams are performed. The result of this research is evaluated by looking at the performance of the models based on accuracy where the result with the highest accuracy is chosen as the solution. In this study, data is data from TripAdvisor and Google reviews using web scraping tools. The findings show that the Support Vector Machine model with 5-fold cross-validation the most suitable classifier with an accuracy of 67.97% compared to Naive Bayes with 61.33% accuracy and Random Forest classifier with 63.55% accuracy. In conclusion, the result of this paper could provide important information in tourism besides determining the suitable algorithm to be used for Sentiment Analysis related to the tourism domain.

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Section: Sentiment Analysismentioning

confidence: 99%

Section: Sentiment Analysismentioning

confidence: 99%

Section: Sentiment Analysismentioning

confidence: 99%

Section: Sentiment Analysismentioning

confidence: 99%

See 2 more Smart Citations

Sentiment classification from reviews for tourism analytics

Haris

Mutalib

Malik

et al. 2023

Int. J. Adv. Intell. Informatics

View full text Add to dashboard Cite

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“…Service quality has been developed in the various industry not to mention in hotel industry with the variable of HOLSERV (Jeon, 2018;Rus et al, 2019;Mei et al, 1999;Subha, 2020;Chen et al, 2010). However, none of those studies focus on the respondents who experienced MICE, hence this study is expected to fill in the gap of using HOLSERV to MICE users.…”

Section: Introductionmentioning

confidence: 99%

The Influence of Customer Service Quality towards Customer Satisfaction and its implication on Loyalty: A Survey on MICE Customers in Hotels

Kumar

Samtani²

2021

FIRM

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This research was conducted to determine the Influence of Customer Service Quality towards Customer Satisfaction and its implication on Loyalty among MICE Customers in Hotels surrounding Bekasi. For service quality, instead of applying the famous SERVQUAL, this study uses the newly build service quality for hotel industry, HOLSERV. Quantitative data analysis was applied to test the relationship among the variables. Prior to that, data were tested for its validity and reliability. Customers of hotels surrounding Bekasi were taken as the respondents with the help of hotel managers who participated in the study. Participants were taken not only those experiencing MICE but also from the employee category assuming the degree of exposure to MICE much higher than students. Structural equation modelling was applied to test the influencing factors among the variables since it has better details in results compare to multiple regression technique. The result shows all the variables in HOLSERV have a significant influence on Customer Satisfaction which leads to Customer loyalty. In addition, customer satisfaction itself has proven to be significant to loyalty.

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