Sentiment Analysis for Hotel Attributes from Online Reviews

Demir, Yunus Emre; DURMAZ, Semih; Elbir, Ahmet; Sigirci, Ibrahim Onur; Di̇ri̇, Banu

doi:10.1109/asyu50717.2020.9259823

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…Zhang et al (2015) used word2vec to cluster the similar features, and demonstrated that this approach can effectively capture the semantic features in selected domain and Chinese language. Demir et al (2020) employed word2vec to represent 11 hotel attributes with words of similar meanings.…”

Section: Topic Extractionmentioning

confidence: 99%

Dynamic Measurement and Evaluation of Hotel Customer Satisfaction Through Sentiment Analysis on Online Reviews

Zhou

Liao

2021

Journal of Organizational and End User Computing

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Both researchers and practitioners have attached great importance to the measurement and evaluation of hotel customer satisfaction. However, there are several problems in the dimensions, methods and conclusions. Thus, it is urgent to standardize the theories, methods, and techniques. The purpose of this study is to propose a dynamic measurement and evaluation framework for hotel customer satisfaction through sentiment analysis on online reviews. The framework consists of five steps: (1)The corpus is obtained from online review sites; (2)From the perspective of managers, the useful texts are recognized; (3)Based on the useful texts, a three-layer index system is created; (4)The center term-based short sentence sentimental orientation (CTSSSO) algorithm is developed to compute emotional intensity, then dynamically measure the customer satisfaction; (5)The dynamic important performance competitor analysis (DIPCA) is adopted for dynamic evaluation of customer satisfaction.The feasibility of our framework was demonstrated through a case study on the online reviews of two five-star hotels.

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Section: Topic Extractionmentioning

confidence: 99%

Dynamic Measurement and Evaluation of Hotel Customer Satisfaction Through Sentiment Analysis on Online Reviews

Zhou

Liao

2021

Journal of Organizational and End User Computing

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“…Various communities have proposed sentiment analysis by using different approaches, including real-world scale data [7], online reviews based on the several factors that are employed [8], or using distributed word representations for aspect-based sentiment analysis in hotel reviews [9]. Automatic labeling of text data is challenging when unstructured heaps of textual data and a significant volume of data.…”

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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Sentiment Analysis for Hotel Attributes from Online Reviews

Cited by 2 publications

References 4 publications

Dynamic Measurement and Evaluation of Hotel Customer Satisfaction Through Sentiment Analysis on Online Reviews

Dynamic Measurement and Evaluation of Hotel Customer Satisfaction Through Sentiment Analysis on Online Reviews

Sentiment Analysis of Hotel User Review using RNN Algorithm

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