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Abstract

Accurate demand forecasting plays a critical role in hotel revenue management. Online reviews have emerged as a viable information source for hotel demand forecasting. However, existing hotel demand forecasting studies leverage only sentiment information from online reviews, leading to capturing insufficient information. Furthermore, prevailing hotel demand forecasting methods either lack explainability or fail to capture local correlations within sequences. In this study, we (1) propose a comprehensive framework consisting of four components: expertise, sentiment, popularity, and novelty (ESPN framework), to investigate the impact of online reviews on hotel demand forecasting; (2) propose a novel dual attention-based long short-term memory convolutional neural network (DA-LSTM-CNN) model to optimize the model effectiveness. We collected online review data from Ctrip.com to evaluate our proposed ESPN framework and DA-LSTM-CNN model. The empirical results show that incorporating features derived from the ESPN improves forecasting accuracy and our DA-LSTM-CNN significantly outperforms the state-of-the-art models. Further, we use a case study to illustrate the explainability of the DA-LSTM-CNN, which could guide future setups for hotel demand forecasting systems. We discuss how stakeholders can benefit from our proposed ESPN framework and DA-LSTM-CNN model.

Source

Journal of the Association for Information Science and Technology. 74(2023) no.9, S.1100-1117

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Abstract

The usefulness of online geological maps is hindered by linguistic barriers. Multilingual geoscience thesauri alleviate linguistic barriers of geological maps. However, the benefits of multilingual geoscience thesauri for online geological maps are less studied. In this regard, we developed a multilingual thesaurus of geological time scale (GTS) to alleviate linguistic barriers of GTS records among online geological maps. We extended the Simple Knowledge Organization System (SKOS) model to represent the ordinal hierarchical structure of GTS terms. We collected GTS terms in seven languages and encoded them into a thesaurus by using the extended SKOS model. We implemented methods of characteristic-oriented term retrieval in JavaScript programs for accessing Web Map Services (WMS), recognizing GTS terms, and making translations. With the developed thesaurus and programs, we set up a pilot system to test recognitions and translations of GTS terms in online geological maps. Results of this pilot system proved the accuracy of the developed thesaurus and the functionality of the developed programs. Therefore, with proper deployments, SKOS-based multilingual geoscience thesauri can be functional for alleviating linguistic barriers among online geological maps and, thus, improving their interoperability.

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Source

Journal of the Association for Information Science and Technology. 69(2018) no.1, S.98-109

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Source

Journal of the Association for Information Science and Technology. 72(2021) no.11, S.1386-1399