Analyzing user reviews in tourism with topic models

Rossetti, Marco; Stella, Fabio; Zanker, Markus

doi:10.1007/s40558-015-0035-y

Cited by 69 publications

(34 citation statements)

References 27 publications

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“…,Fuchs et al (2013;,Höpken et al (2015), andTseng and Won (2016). However, the work byHöpken et al (2015) also applies data mining techniques, such as Decision Trees and Association Rule Mining.When it comes to the aggregation and (sentiment) analysis of user-generated content,Rossetti et al (2016) apply both K-Nearest Neighbor User Based (KNN-UB), K-Nearest Neighbor Item Based (KNN-IB) and Probabilistic Matrix Factorization (PMF) techniques. By contrast, Marine-Roig and Anton Clavé (2015) use parsing and categorizations through a wordfrequency-based Site Content Analyzer.…”

mentioning

confidence: 99%

Business intelligence and big data in hospitality and tourism: a systematic literature review

Mariani

Baggio

Fuchs

et al. 2018

IJCHM

309

231

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Purpose-This study examines the extent to which Business Intelligence and Big Data feature within academic research in hospitality and tourism published until 2016, by identifying research gaps and future developments and designing an agenda for future research. Design/methodology/approach-The study consists of a systematic quantitative literature review of academic articles indexed on the Scopus and Web of Science databases. The articles were reviewed based on the following features: research topic; conceptual and theoretical characterization; sources of data; type of data and size; data collection methods; data analysis techniques; data reporting and visualization. Findings-Findings indicate an increase in hospitality and tourism management literature applying analytical techniques to large quantities of data. However, this research field is fairly fragmented in scope and limited in methodologies and displays several gaps. A conceptual framework that helps to identify critical business problems and links the domains of Business Intelligence and Big Data to tourism and hospitality management and development is missing. Moreover, epistemological dilemmas and consequences for theory development of big datadriven knowledge are still a terra incognita. Last, despite calls for more integration of management and data science, cross-disciplinary collaborations with computer and data scientists are rather episodic and related to specific types of work and research. Research limitations/implications-This work is based on academic articles published before 2017; hence, scientific outputs published after the moment of writing have not been included. A rich research agenda is designed. Originality/value-This study contributes to explore in depth and systematically to what extent hospitality and tourism scholars are aware of and working intendedly on Business Intelligence and Big Data. To the best of our knowledge, it is the first systematic literature review within hospitality and tourism research dealing with Business Intelligence and Big Data.

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mentioning

confidence: 99%

Business intelligence and big data in hospitality and tourism: a systematic literature review

Mariani

Baggio

Fuchs

et al. 2018

IJCHM

309

231

View full text Add to dashboard Cite

show abstract

“…However, achieving high effectiveness in this process constitutes a challenging task in contexts of the great diversity of opinions. Identifying topics is of great importance to determine regarding which issues users are giving their criteria [23], being one of the reasons that some opinion summarization approaches detect topics in their textual analysis [1,8,9,24,25]. Although the resulting summaries are generally focused on aspects or topics, they are mainly identified taking into account only the content of the opinionated texts and do not focus on specific information-context interests.…”

Section: Related Workmentioning

confidence: 99%

“…The aspect-based opinion summarization is one of the main approaches [7], but it would not be very appropriate in contexts where the opinions are not about products or services (e.g., opinions about news). Although summaries generated by several of the reported approaches are focused on specific topics [1,8,9], they are generally identified by looking only at the content in opinionated texts, whereas the context that originates the opinions (e.g., news) is not usually taken into account, being this a weakness. A comprehensive summary of the users' reactions concerning a news article can be crucial due to various reasons, such as (1) understanding the sensitivity/importance of the news, (2) obtaining insights about the diverse opinions of the readers regarding the news, and (3) understanding the key aspects that draw the interest of the readers [10].…”

Section: Introductionmentioning

confidence: 99%

Towards Context-Aware Opinion Summarization for Monitoring Social Impact of News

et al. 2020

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Opinion mining and summarization of the increasing user-generated content on different digital platforms (e.g., news platforms) are playing significant roles in the success of government programs and initiatives in digital governance, from extracting and analyzing citizen’s sentiments for decision-making. Opinion mining provides the sentiment from contents, whereas summarization aims to condense the most relevant information. However, most of the reported opinion summarization methods are conceived to obtain generic summaries, and the context that originates the opinions (e.g., the news) has not usually been considered. In this paper, we present a context-aware opinion summarization model for monitoring the generated opinions from news. In this approach, the topic modeling and the news content are combined to determine the “importance” of opinionated sentences. The effectiveness of different developed settings of our model was evaluated through several experiments carried out over Spanish news and opinions collected from a real news platform. The obtained results show that our model can generate opinion summaries focused on essential aspects of the news, as well as cover the main topics in the opinionated texts well. The integration of term clustering, word embeddings, and the similarity-based sentence-to-news scoring turned out the more promising and effective setting of our model.

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“…Rossetti et al (Rossetti, Stella, & Zanker, 2016) provide a description of topic models with a particular focus on the tourism domain. They propose different application scenarios where the topic models effectively processes textual reviews in order to provide decision support and recommendations to online tourists as well as to build a basis for further analytics (i.e.…”

Section: Latent Dirichlet Allocation and Topic Modelsmentioning

confidence: 99%

Identifying Innovative Idea Proposals with Topic Models—A Case Study from SPA Tourism

Sottocornola

Stella

Symeonidis

et al. 2019

Big Data and Innovation in Tourism, Travel, and Hospitality

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This chapter builds on a dataset where online users of a spa platform participated in an online idea contest providing free-text descriptions of their proposals for spa services. A panel of domain experts annotated these idea descriptions with a score for their innovativeness that serves as ground truth for machine learning experiments. Thus, the contribution lies in the application of topic modeling techniques to freetext idea descriptions in order to automatically identify innovative proposals based on advanced text processing and machine learning. Results of this case study indicate that topic modeling can outperform the ZeroR baseline as well as traditional survey scales for lead user identification and therefore constitute a first step towards exploring this technique for innovation research.

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Analyzing user reviews in tourism with topic models

Cited by 69 publications

References 27 publications

Business intelligence and big data in hospitality and tourism: a systematic literature review

Business intelligence and big data in hospitality and tourism: a systematic literature review

Towards Context-Aware Opinion Summarization for Monitoring Social Impact of News

Identifying Innovative Idea Proposals with Topic Models—A Case Study from SPA Tourism

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