Mining Bilateral Reviews: A Relational Topic Modeling Framework for Transaction Success Prediction in Sharing Economy

In line with the qualitative and quantitative growth of academic papers, it is critical to understand the factors driving citations in scholarly articles. This study discovered the up-to-date academic structure in the tourism and hospitality literature and tested the comprehensive sets of factors driving citation counts using articles published in first-tier hospitality and tourism journals found on the Web of Science. To further test the effects of research topic structure on citation counts, unsupervised topic modeling was conducted with 9910 tourism and hospitality papers published in 12 journals over 10 years. Articles specific to online media and the sharing economy have received numerous citations and that recently published papers with particular research topics (e.g., rural tourism and eco-tourism) were frequently cited. This study makes a major contribution to hospitality and tourism literature by testing the effects of topic structure and topic originality discovered by text mining on citation counts.

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Development of Topic Modeling Framework Using Probabilistic Recurrent Neural Network

International Journal of Advanced Trends in Computer Science and Engineering ◽

10.30534/ijatcse/2019/106842019 ◽

2019 ◽

Vol 6 (4) ◽

pp. 1761-1767

Author(s):

Lakshmi Prasanna P ◽

Keyword(s):

Neural Network ◽

Recurrent Neural Network ◽

Topic Modeling ◽

Modeling Framework

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Familia: A Configurable Topic Modeling Framework for Industrial Text Engineering

Database Systems for Advanced Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-030-73200-4_36 ◽

2021 ◽

pp. 516-528

Author(s):

Di Jiang ◽

Yuanfeng Song ◽

Rongzhong Lian ◽

Siqi Bao ◽

Jinhua Peng ◽

...

Keyword(s):

Topic Modeling ◽

Modeling Framework

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Structural Topic Modeling For Social Scientists: A Brief Case Study with Social Movement Studies Literature, 2005–2017

Social Currents ◽

10.1177/2329496519846505 ◽

2019 ◽

Vol 6 (4) ◽

pp. 307-318 ◽

Cited By ~ 1

Author(s):

Nathan C. Lindstedt

Keyword(s):

Social Movement ◽

Topic Modeling ◽

Research Process ◽

Computer Assisted ◽

Sociological Research ◽

Social Scientists ◽

Modeling Framework ◽

Social Scientific ◽

Structural Topic Modeling

Sociologists frequently make use of language as data in their research using methodologies including open-ended surveys, in-depth interviews, and content analyses. Unfortunately, the ability of researchers to analyze the growing amount of these data declines as the costs and time associated with the research process increases. Topic modeling is a computer-assisted technique that can help social scientists to address these data challenges. Despite the central role of language in sociological research, to date, the field has largely overlooked the promise of automated text analysis in favor of more familiar and more traditional methods. This article provides an overview of a topic modeling framework especially suited for social scientific research. By way of a case study using abstracts from social movement studies literature, a short tutorial from data preparation through data analysis is given for the method of structural topic modeling. This example demonstrates how text analytics can be applied to research in sociology and encourages academics to consider such methods not merely as novel tools, but as useful supplements that can work beside and enhance existing methodologies.

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Mining Bilateral Reviews for Online Transaction Prediction: A Relational Topic Modeling Approach

Information Systems Research ◽

10.1287/isre.2020.0981 ◽

2021 ◽

Author(s):

Jiawei Chen ◽

Yinghui (Catherine) Yang ◽

Hongyan Liu

Keyword(s):

Topic Modeling ◽

Topic Model ◽

Sharing Economy ◽

Data Sets ◽

Real World Data ◽

Document Collections ◽

Modeling Approach ◽

Text Corpora ◽

Topic Structure ◽

Different Sources

In recent years, more and more platforms where both buyers and sellers can write reviews for each other have emerged. These bilateral reviews are important information sources in the decision-making process of both buyers and sellers. In this study, we develop a comprehensive relational topic modeling approach to analyze bilateral reviews for better online transaction prediction. The prediction results will enable the platform to increase the chance that the buyer and seller reach a transaction by presenting buyers with offerings that are more likely to lead to a transaction. Within the framework of the relational topic model, we embed a topic structure with both shared and corpus-specific topics to better handle text corpora generated from different sources. Our model facilitates the extraction of the appropriate topic structure from different document collections that helps enhance the transaction prediction performance. Comprehensive experiments conducted on real-world data sets collected from sharing economy platforms demonstrate that our new model significantly outperforms other alternatives. The robust results obtained from multiple sets of comparisons demonstrate the value of bilateral reviews if they are processed properly. Our approach can be applied to many platforms where bilateral reviews are available.

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What’s yours is mine: exploring customer voice on Airbnb using text-mining approaches

Journal of Consumer Marketing ◽

10.1108/jcm-02-2018-2581 ◽

2019 ◽

Vol 36 (5) ◽

pp. 655-665 ◽

Cited By ~ 10

Author(s):

Jurui Zhang

Keyword(s):

Content Analysis ◽

Text Mining ◽

Topic Modeling ◽

Latent Dirichlet Allocation ◽

Local Community ◽

Negative Emotion ◽

Online Reviews ◽

Sharing Economy ◽

Consumer Reviews ◽

Content Type

Purpose This paper aims to investigate customers’ experiences with Airbnb by text-mining customer reviews posted on the platform and comparing the extracted topics from online reviews between Airbnb and the traditional hotel industry using topic modeling. Design/methodology/approach This research uses text-mining approaches, including content analysis and topic modeling (latent Dirichlet allocation method), to examine 1,026,988 Airbnb guest reviews of 50,933 listings in seven cities in the USA. Findings The content analysis shows that negative reviews are more authentic and credible than positive reviews on Airbnb and that the occurrence of social words is positively related to positive emotion in reviews, but negatively related to negative emotion in reviews. A comparison of reviews on Airbnb and hotel reviews shows unique topics on Airbnb, namely, “late check-in”, “patio and deck view”, “food in kitchen”, “help from host”, “door lock/key”, “sleep/bed condition” and “host response”. Research limitations/implications The topic modeling result suggests that Airbnb guests want to get to know and connect with the local community; thus, help from hosts on ways they can authentically experience the local community would be beneficial. In addition, the results suggest that customers emphasize their interaction with hosts; thus, to improve customer satisfaction, Airbnb hosts should interact with guests and respond to guests’ inquiries quickly. Practical implications Hotel managers should design marketing programs that fulfill customers’ desire for authentic and local experiences. The results also suggest that peer-to-peer accommodation platforms should improve online review systems to facilitate authentic reviews and help guests have a smooth check-in process. Originality/value This study is one of the first to examine consumer reviews in detail in the sharing economy and compare topics from consumer reviews between Airbnb and hotels.

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Determinants of Guest Experience in Airbnb: A Topic Modeling Approach Using LDA

Sustainability ◽

10.3390/su12083402 ◽

2020 ◽

Vol 12 (8) ◽

pp. 3402 ◽

Cited By ~ 1

Author(s):

Ian Sutherland ◽

Kiattipoom Kiatkawsin

Keyword(s):

New York ◽

New York City ◽

Topic Modeling ◽

Latent Dirichlet Allocation ◽

Sharing Economy ◽

General Evaluation ◽

Ward Clustering ◽

Accommodation Sector ◽

Complex Relationships

This study inductively analyzes the topics of interest that drive customer experience and satisfaction within the sharing economy of the accommodation sector. Using a dataset of 1,086,800 Airbnb reviews across New York City, the text is preprocessed and latent Dirichlet allocation is utilized in order to extract 43 topics of interest from the user-generated content. The topics fall into one of several categories, including the general evaluation of guests, centralized or decentralized location attributes of the accommodation, tangible and intangible characteristics of the listed units, management of the listing or unit, and service quality of the host. The deeper complex relationships between topics are explored in detail using hierarchical Ward Clustering.

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