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

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.

scholarly journals What Are the Drivers of Citations?: Application in Tourism and Hospitality Journals

2021 ◽  
Vol 11 (19) ◽  
pp. 9288
Author(s):  
Eunhye Park ◽  
Woohyuk Kim
Keyword(s):  
Topic Modeling ◽  
Web Of Science ◽  
Sharing Economy ◽  
Online Media ◽  
Research Topics ◽  
Qualitative And Quantitative ◽  
Hospitality And Tourism ◽  
Citation Counts ◽  
Tourism And Hospitality ◽  
Topic Structure

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.

A Topic Modeling Approach to Study the Impact of Manager Interventions on Design Team Cognition

2020 ◽  
Author(s):  
Joshua T. Gyory ◽  
Kenneth Kotovsky ◽  
Jonathan Cagan
Keyword(s):  
Problem Solving ◽  
Topic Modeling ◽  
Process Management ◽  
Topic Model ◽  
Design Team ◽  
Design Cognition ◽  
Team Cognition ◽  
Modeling Approach ◽  
Before And After ◽  
The Impact

Abstract In order to computationally study design cognition under design process management, this work utilizes a topic modeling approach to analyze design team discourse during problem-solving. The particular experimental design, from previous work by the authors, places one of the design team conditions under the guidance of a human process manager. In that work, teams under this guidance outperformed the unmanaged teams in terms of their design solutions. This opens the opportunity to not only model design discourse during problem solving, but also explore the impact of process manager interventions and their impact on design cognition. Utilizing this approach, a topic model is trained on discourse of human designers, for both managed and unmanaged teams, collaboratively solving a design problem. Results show that the two team conditions significantly differ in a number of the extracted topics, and in particular, those topics that most pertain to the manager interventions. Furthermore, a before and after analysis of the topic-motivated interventions, reveals that the process manager interventions significantly shift the topic mixture of the team members’ discourse toward that of the interventions immediately after they are provided. Together, these results not only corroborate the effect of the process manager interventions on design team discourse and cognition, but provide promise in the computational detection and facilitation of design interventions based on real-time discourse data.

scholarly journals Applying Word Co-Occurrence Graph in Enhancing LDA Model for Topic Discovering in Large-Scaled Text Corpus

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1366-1371
Keyword(s):  
Statistical Analysis ◽  
Topic Modeling ◽  
Topic Model ◽  
Bag Of Words ◽  
Text Corpus ◽  
Document Collections ◽  
Text Document ◽  
Novel Approach ◽  
Proposed Model ◽  
Low Performance

Topic modeling, such as LDA is considered as a useful tool for the statistical analysis of text document collections and other text-based data. Recently, topic modeling becomes an attractive researching field due to its wide applications. However, there are remained disadvantages of traditional topic modeling like as LDA due the shortcoming of bag-of-words (BOW) model as well as low-performance in handle large text corpus. Therefore, in this paper, we present a novel approach of topic model, called LDA-GOW, which is the combination of word co-occurrence, also called: graph-of-words (GOW) model and traditional LDA topic discovering model. The LDA-GOW topic model not only enable to extract more informative topics from text but also be able to leverage the topic discovering process from large-scaled text corpus. We test our proposed model in comparing with the traditional LDA topic model, within several standardized datasets, include: WebKB, Reuters-R8 and annotated scientific documents which are collected from ACM digital library to demonstrate the effectiveness of our proposed model. For overall experiments, our proposed LDA-GOW model gains approximately 70.86% in accuracy.

scholarly journals Improved Parsimonious Topic Modeling Based on the Bayesian Information Criterion

Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 326
Author(s):  
Hang Wang ◽  
David Miller
Keyword(s):  
Bayesian Information Criterion ◽  
Goodness Of Fit ◽  
Learning Strategy ◽  
Topic Model ◽  
Information Criterion ◽  
Model Complexity ◽  
Small Subset ◽  
Data Sets ◽  
Model Learning ◽  
Text Corpora

In a previous work, a parsimonious topic model (PTM) was proposed for text corpora. In that work, unlike LDA, the modeling determined a subset of salient words for each topic, with topic-specific probabilities, with the rest of the words in the dictionary explained by a universal shared model. Further, in LDA all topics are in principle present in every document. In contrast, PTM gives sparse topic representation, determining the (small) subset of relevant topics for each document. A customized Bayesian information criterion (BIC) was derived, balancing model complexity and goodness of fit, with the BIC minimized to jointly determine the entire model—the topic-specific words, document-specific topics, all model parameter values, and the total number of topics—in a wholly unsupervised fashion. In the present work, several important modeling and algorithm (parameter learning) extensions of PTM are proposed. First, we modify the BIC objective function using a lossless coding scheme with low modeling cost for describing words that are non-salient for all topics—such words are essentially identified as wholly noisy/uninformative. This approach increases the PTM’s model sparsity, which also allows model selection of more topics and with lower BIC cost than the original PTM. Second, in the original PTM model learning strategy, word switches were updated sequentially, which is myopic and susceptible to finding poor locally optimal solutions. Here, instead, we jointly optimize all the switches that correspond to the same word (across topics). This approach jointly optimizes many more parameters at each step than the original PTM, which in principle should be less susceptible to finding poor local minima. Results on several document data sets show that our proposed method outperformed the original PTM model with respect to multiple performance measures, and gave a sparser topic model representation than the original PTM.

Latent Dirichlet allocation-based temporal summarization

2019 ◽  
Vol 15 (1) ◽  
pp. 83-102 ◽  
Author(s):  
Ahmed Amir Tazibt ◽  
Farida Aoughlis
Keyword(s):  
Topic Modeling ◽  
Design Methodology ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
External Source ◽  
Decision Makers ◽  
Content Type ◽  
Available Information ◽  
Different Sources ◽  
Dirichlet Allocation

Purpose During crises such as accidents or disasters, an enormous volume of information is generated on the Web. Both people and decision-makers often need to identify relevant and timely content that can help in understanding what happens and take right decisions, as soon it appears online. However, relevant content can be disseminated in document streams. The available information can also contain redundant content published by different sources. Therefore, the need of automatic construction of summaries that aggregate important, non-redundant and non-outdated pieces of information is becoming critical. Design/methodology/approach The aim of this paper is to present a new temporal summarization approach based on a popular topic model in the information retrieval field, the Latent Dirichlet Allocation. The approach consists of filtering documents over streams, extracting relevant parts of information and then using topic modeling to reveal their underlying aspects to extract the most relevant and novel pieces of information to be added to the summary. Findings The performance evaluation of the proposed temporal summarization approach based on Latent Dirichlet Allocation, performed on the TREC Temporal Summarization 2014 framework, clearly demonstrates its effectiveness to provide short and precise summaries of events. Originality/value Unlike most of the state of the art approaches, the proposed method determines the importance of the pieces of information to be added to the summaries solely relying on their representation in the topic space provided by Latent Dirichlet Allocation, without the use of any external source of evidence.

scholarly journals Measuring Equity-Promoting Behaviors in Digital Teaching Simulations: A Topic Modeling Approach

2020 ◽  
Author(s):  
Joshua Littenberg-Tobias ◽  
Justin Reich ◽  
Elizabeth Borneman
Keyword(s):  
Racial Disparities ◽  
Behavioral Change ◽  
Topic Modeling ◽  
Topic Model ◽  
Reference Group ◽  
Online Course ◽  
Modeling Approach ◽  
Discriminatory Behavior ◽  
Equity And Inclusion ◽  
Structural Topic Model

Diversity, equity, and inclusion (DEI) issues are urgent in education, given the widespread evidence of discriminatory behavior and widening racial disparities. Although DEI trainings can change participants’ attitudes they have minimal effects on behaviors. Simulations are a promising approach to address this gap between attitudinal and behavioral change. We developed an online course for educators (N = 963) that included a series of equity simulations and applied the structural topic model (STM), to identify alignment between participants' simulated behavior and equity attitudes on surveys. STM identified meaningful topics within participants’ simulation responses and that the prevalence of these topics varied by equity attitudes. We also measured changes in participants' behaviors over different, successive, simulations, by comparing with a reference group of high equity-oriented participants. Participants made significant shifts in the simulations toward equity-promoting behaviors (ES = 0.99), which corresponded with changes in equity-oriented attitudes (ES=0.74) and self-reported equity-promoting behaviors (ES=0.30).

scholarly journals AN IMPROVED PATTERN BASED LDA TOPIC MODELING FOR BUSINESS INTELLIGENCE

2021 ◽  
Vol 9 (2) ◽  
pp. 404-409
Author(s):  
K Prashant Gokul, Et. al.
Keyword(s):  
Topic Modeling ◽  
Information Needs ◽  
Pattern Mining ◽  
Topic Model ◽  
Information Filtering ◽  
Topic Models ◽  
Model Learning ◽  
Text Corpora ◽  
Exploratory Data ◽  
Made In

Topic models give a helpful strategy to dimensionality decrease and exploratory data analysis in huge text corpora. Most ways to deal with topic model learning have been founded on a greatest likelihood objective. Proficient algorithms exist that endeavor to inexact this target, yet they have no provable certifications. As of late, algorithms have been presented that give provable limits, however these algorithms are not down to earth since they are wasteful and not hearty to infringement of model presumptions. In this work, we propose to consolidate the statistical topic modeling with pattern mining strategies to produce pattern-based topic models to upgrade the semantic portrayals of the conventional word based topic models. Using the proposed pattern-based topic model, clients' inclinations can be modeled with different topics and every one of which is addressed with semantically rich patterns. A tale information filtering model is proposed here. In information filtering model client information needs are made in terms of different topics where every topic is addressed by patterns. The calculation produces results similar to the best executions while running significant degrees quicker.

TrustSVD: A Novel Trust-Based Matrix Factorization Model with User Trust and Item Ratings

2017 ◽  
Vol 7 (11) ◽  
pp. 7 ◽  
Author(s):  
K Sobha Rani
Keyword(s):  
Matrix Factorization ◽  
Social Trust ◽  
State Of The Art ◽  
Data Sets ◽  
Real World Data ◽  
Recommendation Algorithm ◽  
Active User ◽  
Factorization Model ◽  
The Social ◽  
Matrix Factorization Technique

Collaborative filtering suffers from the problems of data sparsity and cold start, which dramatically degrade recommendation performance. To help resolve these issues, we propose TrustSVD, a trust-based matrix factorization technique. By analyzing the social trust data from four real-world data sets, we conclude that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. Hence, we build on top of a state-of-the-art recommendation algorithm SVD++ which inherently involves the explicit and implicit influence of rated items, by further incorporating both the explicit and implicit influence of trusted users on the prediction of items for an active user. To our knowledge, the work reported is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that our approach TrustSVD achieves better accuracy than other ten counterparts, and can better handle the concerned issues.

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