Comparison of Machine Learning Classification Algorithm on Hotel Review Sentiment Analysis (Case Study: Luminor Hotel Pecenongan)

Miharja, Jaja; Putra, Jordy Lasmana; Hadianto, Nur

doi:10.33480/pilar.v16i1.1131

Cited by 2 publications

(4 citation statements)

References 5 publications

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“…According to this study, when the Support Vector Machine model was validated using 10-Fold Cross Validation, it had the best accuracy of 81.75 percent. This success demonstrates that SVM is the best model out of the three that were suggested [13].…”

Section: Literature Reviewmentioning

confidence: 88%

“…Kadhim [10], V. B. Vaghela, B. M. Jadav [11], M. Karim et al [12], Miharja, R.S. et al [13], Jagdale et al [14].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Jagdale et al compared the classification algorithms Naive Bayes (NB), SVM, and Decision Tree in their discussion of the same topic (DT). The support vector machine (SVM) algorithm test yielded the best accuracy results, with a score of 81.77 percent [13].…”

Section: A Performance Evaluation Analysismentioning

confidence: 99%

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Performance Evaluation of Classification Algorithm for Movie Review Sentiment Analysis

Sutriawan¹,

Andono²,

Muljono³

et al. 2023

IJC

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The majority of the current research on sentiment analysis, which covers topics like political reviews, movie reviews, and product reviews, has developed quickly. The classification and clustering stage of sentiment analysis research involves a number of subjects. Some of them cover text classification comparison research and algorithm performance optimization. An intricate issue in sentiment analysis research is dealing with unstructured or semi-structured data. The sentiment analysis procedure and improving the efficacy of the classifier’s algorithm are both hampered by unstructured data. In order to manage unstructured data successfully and provide accurate and relevant information, unique strategies are required. The proposed classification model performance evaluation using Support Vector Machine, Naive Bayes, K-Nearest Neighbor, and Decision Tree is specifically covered in this paper. According to the study’s findings, SVM has an accuracy rate of 96% and Naive Bayes is 86%. While the decision tree’s gain accuracy is 78 percent and the kNN classification model’s gain accuracy is 78 percent, respectively. The test results demonstrate that SVM is superior to other classification models in terms of accuracy performance.

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Section: Literature Reviewmentioning

confidence: 88%

“…Kadhim [10], V. B. Vaghela, B. M. Jadav [11], M. Karim et al [12], Miharja, R.S. et al [13], Jagdale et al [14].…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Performance Evaluation of Classification Algorithm for Movie Review Sentiment Analysis

Sutriawan¹,

Andono²,

Muljono³

et al. 2023

IJC

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show abstract

“…Analyzing user sentiment can identify elements that help hotel providers understand user behavior more, encourage more consumers to use their services, and anticipate future behavior [12]. The primary goal of sentiment analysis is to determine if an output is good or negative, as well as to look for differences in the text or user-specific data in a dataset [13].…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis of Hotel User Review using RNN Algorithm

Utami¹

2021

ijicom

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Sentiment analysis in user review is a growing research area at the current time. Usually, the website becomes a source of data in knowing the quality of the hotel services, and the provider can utilize the review for monitoring and evaluation. However, determining the positive or negative sentiment of a user review in unstructured textual data takes a long time. As a result, we present a model to classify positive or negative sentiment in user reviews in this article. This study suggests the RNN method in building an effective model to classify user sentiment. Based on the experiment, our model can produce accurate results in organizing hotel reviews. Furthermore, the proposed method achieved a higher evaluation metrics score with an f1-score of 91.0%.

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Comparison of Machine Learning Classification Algorithm on Hotel Review Sentiment Analysis (Case Study: Luminor Hotel Pecenongan)

Cited by 2 publications

References 5 publications

Performance Evaluation of Classification Algorithm for Movie Review Sentiment Analysis

Performance Evaluation of Classification Algorithm for Movie Review Sentiment Analysis

Sentiment Analysis of Hotel User Review using RNN Algorithm

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