Predictive Accuracy of Sentiment Analytics for Tourism: A Metalearning Perspective on Chinese Travel News

Fu, Yu Can; Hao, Jie; Li, Xiang; Hsu, Cheng

doi:10.1177/0047287518772361

Cited by 42 publications

(54 citation statements)

References 66 publications

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“…Predictive analytics uses models to forecast the future (Fu et al, 2018), as data models could quantify the likelihood that a particular person will do something in the foreseeable future -whether it is defaulting on a loan, upgrading to a higher level of cable service or seeking another job (Camilleri, 2015;Siegel and Davenport, 2013). It may appear that predictive analytics anticipates human behaviours that have not happened as yet (Fu et al, 2018). For instance, predictive tools and smart cards have enabled Singapore Land Transit Authority to provide a more convenient transportation system to commuters and leisure passengers.…”

Section: Analysing Datamentioning

confidence: 99%

The use of data-driven technologies for customer-centric marketing

Camilleri¹

2020

IJBDM

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The latest technologies are shifting how businesses capture, analyse and distribute data from the individual users' online activity. Therefore, this contribution critically reviews the latest developments on big data analytics and programmatic advertising. Moreover, it sheds light on the use of blockchain; as this distributed ledger technology provides secure, verified transactions among marketplace stakeholders. The findings suggest that the service providers are increasingly utilising data-driven technologies including programmatic advertising tools to target and re-target individuals online or on their mobile. However, individuals and organisations are becoming increasingly aware on data protection issues, as they often block marketers from tracking them and serving them ads. In conclusion, this contribution puts forward a theoretical framework that explains how, why, where and when practitioners are capturing, analysing and distributing data. In sum, it implies that the data-driven technologies are facilitating the businesses' customer-centric marketing.

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Section: Analysing Datamentioning

confidence: 99%

The use of data-driven technologies for customer-centric marketing

Camilleri¹

2020

IJBDM

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“…Sentiment classification with machine learning methods is mostly based on supervised learning and relies on the completeness of the labeled training corpus, which is a classification method about features. This research [101] pointed out that feature extraction, feature weight, and sentiment classifier are three essential design elements that affect the accuracy of text sentiment classification. Based on this, sentiment classification with machine learning is mainly carried out around these elements.…”

Section: Sentiment Analysismentioning

confidence: 99%

A Review of Text Corpus-Based Tourism Big Data Mining

Zhang

et al. 2019

Applied Sciences

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With the massive growth of the Internet, text data has become one of the main formats of tourism big data. As an effective expression means of tourists' opinions, text mining of such data has big potential to inspire innovations for tourism practitioners. In the past decade, a variety of text mining techniques have been proposed and applied to tourism analysis to develop tourism value analysis models, build tourism recommendation systems, create tourist profiles, and make policies for supervising tourism markets. The successes of these techniques have been further boosted by the progress of natural language processing (NLP), machine learning, and deep learning. With the understanding of the complexity due to this diverse set of techniques and tourism text data sources, this work attempts to provide a detailed and up-, destination image analysis, market demand, etc. Our work also provides guidelines for constructing new tourism big data applications and outlines promising research areas in this field for incoming years. Appl. Sci. 2019, 9, 3300 2 of 27 the travel. In order to utilize this user-generated content properly and further to meet the needs of tourists and promote the tourism industry, we need to analyze and exploit tourists' needs and opinions, and then identify the problems of tourism services or destinations, which has become a new path for tourism development. Besides, as tourism needs become increasingly personalized, visitors begin to pursue self-likeness, self-worth, and diversified travel experiences, and they are no longer willing to endure delays or waits. How to recognize and respond to visitors' behaviors and needs quickly and identify potential customers have become essential factors for the success of tourism stakeholders. By exploiting the subjective information contained in tourism text data, we can assist tourism stakeholders to provide better services for tourists.A large number of text mining techniques have been proposed and applied to tourism text data analysis for creating tourist profiles [8][9][10][11][12][13][14][15] and making effective market supervision [16][17][18][19][20][21][22][23][24][25]. These approaches exploit a variety of text representation strategies [26][27][28][29][30][31][32] and use different NLP techniques for topic extraction [33], text classification [34], sentiment analysis [35], and text clustering [36]. Moreover, while aiming to make computers understand human language, NLP has become the essential tool for text data analysis and is undergoing fast-pace growing based on the applications of deep learning in word embedding, syntax analysis, machine translation, and text understanding. Machine learning-based NLP techniques have been widely used in tourism text analysis, with superior results [19,25]. In addition, due to its high capability for extracting selective and invariant features from texts, and its independency of prior knowledge and linguistic resources, deep learning has been reported to achieve higher performance than other approaches on many NL...

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“…Descriptive analytics focuses on what happened in the past and why. Predictive analytics uses models to forecast the future (Fu et al, 2018), as they could quantify the likelihood that a particular person will do something -whether it is defaulting on a loan, upgrading to a higher level of cable service or seeking another job (Siegel and Davenport, 2013). It may appear that predictive analytics anticipates human behaviours that have not happened yet (Fu et al, 2018).…”

Section: Analysing Datamentioning

confidence: 99%

“…Predictive analytics uses models to forecast the future (Fu et al, 2018), as they could quantify the likelihood that a particular person will do something -whether it is defaulting on a loan, upgrading to a higher level of cable service or seeking another job (Siegel and Davenport, 2013). It may appear that predictive analytics anticipates human behaviours that have not happened yet (Fu et al, 2018). For instance, predictive tools and smart cards enabled Singapore Land Transit Authority to provide a more convenient transportation system to commuters and leisure passengers.…”

Section: Analysing Datamentioning

confidence: 99%

The use of data-driven technologies in tourism marketing

Camilleri¹

2019

Entrepreneurship, Innovation and Inequality

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Predictive Accuracy of Sentiment Analytics for Tourism: A Metalearning Perspective on Chinese Travel News

Cited by 42 publications

References 66 publications

The use of data-driven technologies for customer-centric marketing

The use of data-driven technologies for customer-centric marketing

A Review of Text Corpus-Based Tourism Big Data Mining

The use of data-driven technologies in tourism marketing

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