From Hotel Reviews to City Similarities: A Unified Latent-Space Model

Cagliero, Luca; Quatra, Moreno La; Apiletti, Daniele

doi:10.3390/electronics9010197

Cited by 12 publications

(8 citation statements)

References 28 publications

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“…One of the drawbacks was that they only used the TF-IDF (Term Frequency Inverse Document Frequency) feature, resulting in an accuracy of 0.88. Cagliero et al [13] developed a method based on the Latent-space model on a sentence-level dataset, related to hotel reviews consisting of 200,000 reviews. They adopted the Google word representation model for transferring the list of words from high to low dimensions, called BERT (Bidirectional Encoder Representations from Transformers) pre-trained language embeddings model.…”

Section: Text Sentiment Analysismentioning

confidence: 99%

“…Our approach − Other approach Other approach. * 100 (13) This study is aimed at measuring the attitudes, sentiments, and fake news towards COVID-19, based on the education sector, which can be tracked almost instantaneously, of two different keywords that are used in this epidemic (COVID-19 and E-learning). The patterns of connectivity are required between very positive, positive, neutral, negative, and very negative labels, related to people's opinions, especially when the people try to evaluate the learning techniques (detail is presented in Figure 10).…”

Section: Evaluation and Comparisonmentioning

confidence: 99%

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Topic Modeling and Sentiment Analysis of Online Education in the COVID-19 Era Using Social Networks Based Datasets

2022

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Sentiment Analysis (SA) is a technique to study people’s attitudes related to textual data generated from sources like Twitter. This study suggested a powerful and effective technique that can tackle the large contents and can specifically examine the attitudes, sentiments, and fake news of “E-learning”, which is considered a big challenge, as online textual data related to the education sector is considered of great importance. On the other hand, fake news and misinformation related to COVID-19 have confused parents, students, and teachers. An efficient detection approach should be used to gather more precise information in order to identify COVID-19 disinformation. Tweet records (people’s opinions) have gained significant attention worldwide for understanding the behaviors of people’s attitudes. SA of the COVID-19 education sector still does not provide a clear picture of the information available in these tweets, especially if this misinformation and fake news affect the field of E-learning. This study has proposed denoising AutoEncoder to eliminate noise in information, the attentional mechanism for a fusion of features as parts where a fusion of multi-level features and ELM-AE with LSTM is applied for the task of SA classification. Experiments show that our suggested approach obtains a higher F1-score value of 0.945, compared with different state-of-the-art approaches, with various sizes of testing and training datasets. Based on our knowledge, the proposed model can learn from unified features set to obtain good performance, better results than one that can be learned from the subset of features.

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Section: Text Sentiment Analysismentioning

confidence: 99%

Section: Evaluation and Comparisonmentioning

confidence: 99%

Topic Modeling and Sentiment Analysis of Online Education in the COVID-19 Era Using Social Networks Based Datasets

2022

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“…POI data have been used as a source to study the spatial relationships of hotels [37]. For example, Fang et al [38] studied the correlation between hotel scores and three main location-related features: accessibility to points of interest.…”

Section: Point Of Interestmentioning

confidence: 99%

Analysis of Influencing Factors and Distribution Simulation of Budget Hotel Room Pricing Based on Big Data and Machine Learning from a Spatial Perspective

Song

2022

Sustainability

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The goal of investors in the hotel business is to maximize profits, and the price is an important means of achieving this goal. This has attracted many scholars to study the spatiotemporal relationship between hotel room prices and their possible influencing factors from different perspectives. However, most existing studies adopt the linear assumption of the hedonic model, with limited features and a lack of feature selection procedures. Additionally, there are few forecasts of hotel pricing from a spatial perspective. To overcome these gaps, this study adopts linear and nonlinear machine learning methods based on the “big data” of Sanya City to explore the influencing factors of budget hotel pricing. Based on the spatial perspective, 81 potential factors were considered. They are further selected using a feature extraction model called recursive feature elimination. Six machine-learning algorithms were evaluated and compared: random forest, extreme gradient boosting, multi-linear regression, support vector regression, multilayer perceptron regression, and K-nearest neighbor regression. The optimal value was used to further calculate the feature importance. They disclosed 40 important impact characteristics and predicted the spatial distribution of hotel pricing.

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“…Sharma et al [12] used NLP (Natural Language Processing) in their work to determine the rating of the hotel used by the previous customers. The authors of [13] proposed the use of a unified deep NLP model, which analyzes sentences in reviews. They make use of BERT embedding to transform the raw text data into a unified review-POI latent space.…”

Section: Related Workmentioning

confidence: 99%

“…The experiments used a dataset collected from TripAdvisor. In [13], the authors proposed the use of a unified deep natural language processing (NLP) model which analyzes sentences in reviews and uses public TripAdvisor hotel-review datasets to validate the approach experimentally. They addressed the challenge of investigating the similarities and dissimilarities between cities by considering the textual reviews and numerical ratings of the hotels and their correlation with the nearby P.O.I.s.…”

Section: Related Workmentioning

confidence: 99%

An Effective Hotel Recommendation System through Processing Heterogeneous Data

et al. 2021

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Recommendation systems have recently gained a lot of popularity in various industries such as entertainment and tourism. They can act as filters of information by providing relevant suggestions to the users through processing heterogeneous data from different networks. Many travelers and tourists routinely rely on textual reviews, numerical ratings, and points of interest to select hotels in cities worldwide. To attract more customers, online hotel booking systems typically rank their hotels based on the recommendations from their customers. In this paper, we present a framework that can rank hotels by analyzing hotels’ customer reviews and nearby amenities. In addition, a framework is presented that combines the scores generated from user reviews and surrounding facilities. We perform experiments using datasets from online hotel booking platforms such as TripAdvisor and Booking to evaluate the effectiveness and applicability of the proposed framework. We first store the keywords extracted from reviews and assign weights to each considered unigram and bigram keywords and, then, we give a numerical score to each considered keyword. Finally, our proposed system aggregates the scores generated from the reviews and surrounding environments from different categories of the facilities. Experimental results confirm the effectiveness of the proposed recommendation framework.

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From Hotel Reviews to City Similarities: A Unified Latent-Space Model

Cited by 12 publications

References 28 publications

Topic Modeling and Sentiment Analysis of Online Education in the COVID-19 Era Using Social Networks Based Datasets

Topic Modeling and Sentiment Analysis of Online Education in the COVID-19 Era Using Social Networks Based Datasets

Analysis of Influencing Factors and Distribution Simulation of Budget Hotel Room Pricing Based on Big Data and Machine Learning from a Spatial Perspective

An Effective Hotel Recommendation System through Processing Heterogeneous Data

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