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Assessing text mining algorithm outcomes 评估文本挖掘算法的结果
Journal of Business Analytics Pub Date : 2020-06-25 DOI: 10.1080/2573234x.2020.1785342
Triss Ashton, Nicholas E. Evangelopoulos, A. Paswan, V. Prybutok, R. Pavur
{"title":"Assessing text mining algorithm outcomes","authors":"Triss Ashton, Nicholas E. Evangelopoulos, A. Paswan, V. Prybutok, R. Pavur","doi":"10.1080/2573234x.2020.1785342","DOIUrl":"https://doi.org/10.1080/2573234x.2020.1785342","url":null,"abstract":"ABSTRACT There is a surge in the development of decision-oriented analysis tools intended to extract actionable information from text. These tools integrate various text-mining methods that were performance tested in a manner that was often biased toward the new system. Those tests primarily utilised descriptive measurement criteria and test datasets that are inconsistent with most business corpora. We propose and test a user-oriented judgment approach that allows testing under controlled customer-oriented corpora and generates effect size measures. To illustrate the approach, customer relations data was analysed by latent semantic analysis and latent Dirichlet analysis with results evaluated by prospective business analysts. Reporting includes comparisons of results with published literature. While the research centres on the context-region text-mining systems, literature comparisons include word-embedding methods. The analysis concludes that none of the systems reviewed possess a repeatable statistical advantage over the others. Instead, distribution attributes, algorithm configuration, and the evaluation task drive results.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":"15 1","pages":"107 - 121"},"PeriodicalIF":0.0,"publicationDate":"2020-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76720900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
A hybrid data analytics approach for high-performance concrete compressive strength prediction 高性能混凝土抗压强度预测的混合数据分析方法
Journal of Business Analytics Pub Date : 2020-05-05 DOI: 10.1080/2573234x.2020.1760741
Serhat Simsek, Mehmet Gumus, Mohamed S. Khalafalla, T. B. Issa
{"title":"A hybrid data analytics approach for high-performance concrete compressive strength prediction","authors":"Serhat Simsek, Mehmet Gumus, Mohamed S. Khalafalla, T. B. Issa","doi":"10.1080/2573234x.2020.1760741","DOIUrl":"https://doi.org/10.1080/2573234x.2020.1760741","url":null,"abstract":"ABSTRACT Contrary to the popular belief cited in the literature, the proposed data analytics technique shows that multiple linear regression (MLR) can achieve as high a predictive power as some of the black box models when the necessary interventions are implemented pertaining to the regression diagnostic. Such an MLR model can be utilised to design an optimal concrete mix, as it provides the explicit and accurate relationships between the HPC components and the expected compressive strength. Moreover, the proposed study offers a decision support tool incorporating the Extreme Gradient Boosting (XGB) model to bridge the gap between black-box models and practitioners. The tool can be used to make faster, more data-driven, and accurate managerial decisions without having any expertise in the required fields, which would reduce a substantial amount of time, cost, and effort spent on measurement procedures of the compressive strength of HPC.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":"34 1","pages":"158 - 168"},"PeriodicalIF":0.0,"publicationDate":"2020-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75612118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 6
Management of analytics-as-a-service - results from an action design research project “分析即服务”的管理源于一个行动设计研究项目
Journal of Business Analytics Pub Date : 2020-01-02 DOI: 10.1080/2573234X.2020.1740616
Christian Dremel, Emanuel Stoeckli, Jochen Wulf
{"title":"Management of analytics-as-a-service - results from an action design research project","authors":"Christian Dremel, Emanuel Stoeckli, Jochen Wulf","doi":"10.1080/2573234X.2020.1740616","DOIUrl":"https://doi.org/10.1080/2573234X.2020.1740616","url":null,"abstract":"ABSTRACT The ability to generate business-relevant information from data and to exploit it to improve business processes, decision-making, products, and services (business analytics) is a key success factor for businesses today. Answering the call for further research on success-relevant practices and instruments for managing business analytics, we report on the results of a three-year action design research (ADR) project at a global car manufacturer. Drawing on the socio-technical systems theory, we identify seven meta-requirements and specify four principles for the design of an instrument to manage Analytics-as-a-Service (AaaS) portfolios. Our results reinforce the importance of coordinating different socio-technical components in business analytics initiatives and demonstrate how concrete management instruments, such as a proposed portfolio management tool, contribute to socio-technical alignment. For practitioners, the documented design components provide guidance on how to design and implement similar instruments that support the systematic management of AaaS portfolios.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":"92 1","pages":"1 - 16"},"PeriodicalIF":0.0,"publicationDate":"2020-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82090860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
Refund fraud analytics for an online retail purchases 在线零售购物退款欺诈分析
Journal of Business Analytics Pub Date : 2020-01-02 DOI: 10.1080/2573234X.2020.1776164
Shylu John, Bhavin J. Shah, P. Kartha
{"title":"Refund fraud analytics for an online retail purchases","authors":"Shylu John, Bhavin J. Shah, P. Kartha","doi":"10.1080/2573234X.2020.1776164","DOIUrl":"https://doi.org/10.1080/2573234X.2020.1776164","url":null,"abstract":"ABSTRACT Online shopping is growing fast across the globe and so are its complexities. Fraud is a complicated phenomenon and its mitigation is critical for running a smooth business. The case considered for the present study is fraud mitigation in return – refund process managed by the customer services of an online retail business. Predictive analytics approach was used to identify early indicators of agent refund fraud – a rare event. The technique used to solve the problem was a Penalised Likelihood based Logistic Regression model. The proposed model allowed the business to select top 5% sample of refund transactions with a higher likelihood of fraud as indicated and queue them for an audit. Implementation of this model resulted in an incremental lift in fraud capture rate.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":"35 1","pages":"56 - 66"},"PeriodicalIF":0.0,"publicationDate":"2020-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80392732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Deriving topic-related and interaction features to predict top attractive reviews for a specific business entity 获取与主题相关的功能和交互功能，以预测特定业务实体的热门评论
Journal of Business Analytics Pub Date : 2020-01-02 DOI: 10.1080/2573234X.2020.1768808
Eunjung Lee, Huimin Zhao
{"title":"Deriving topic-related and interaction features to predict top attractive reviews for a specific business entity","authors":"Eunjung Lee, Huimin Zhao","doi":"10.1080/2573234X.2020.1768808","DOIUrl":"https://doi.org/10.1080/2573234X.2020.1768808","url":null,"abstract":"ABSTRACT As large volumes of online reviews are being generated, both online businesses and customers are confronted with big data challenges. Previous studies have developed various methods to predict the helpfulness of online reviews. These methods have disregarded the aspects of the business entities when dealing with datasets for prediction and evaluation and have not considered interactions between a review and the target business entity. In this paper, we propose a novel method to predict the top attractive reviews for a specific business entity. We also propose topic-related features to characterise the topics in a review and interaction features to reflect relationships between a review and the business entity it covers. Our empirical evaluation shows the utility of our proposed method and features.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":"1 1","pages":"17 - 31"},"PeriodicalIF":0.0,"publicationDate":"2020-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83495989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Privacy preserving targeted advertising and recommendations 隐私保护有针对性的广告和推荐
Journal of Business Analytics Pub Date : 2020-01-02 DOI: 10.1080/2573234X.2020.1763862
Theja Tulabandhula, Shailesh Vaya, Aritra Dhar
{"title":"Privacy preserving targeted advertising and recommendations","authors":"Theja Tulabandhula, Shailesh Vaya, Aritra Dhar","doi":"10.1080/2573234X.2020.1763862","DOIUrl":"https://doi.org/10.1080/2573234X.2020.1763862","url":null,"abstract":"ABSTRACT Recommendation systems form the centerpiece of a rapidly growing trillion dollar online advertisement industry. Curating and storing profile information of users on web portals can seriously breach their privacy. Modifying such systems to achieve private recommendations without extensive redesign of the recommendation process typically requires communication of large encrypted information, making the whole process inefficient due to high latency. In this paper, we present an efficient recommendation system redesign, in which user profiles are maintained entirely on their device/web-browsers, and appropriate recommendations are fetched from web portals in an efficient privacy-preserving manner. We base this approach on precomputing compressed data structures from historical data and running low latency lookups when providing recommendations in real-time.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":"1 1","pages":"32 - 55"},"PeriodicalIF":0.0,"publicationDate":"2020-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91005369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Artificial neural network incorporated decision support tool for point velocity prediction 人工神经网络结合决策支持工具进行点速预测
Journal of Business Analytics Pub Date : 2020-01-02 DOI: 10.1080/2573234X.2020.1751569
Serhat Simsek, Onur Genç, Abdullah Albizri, S. Dinç, Bilal Gonen
{"title":"Artificial neural network incorporated decision support tool for point velocity prediction","authors":"Serhat Simsek, Onur Genç, Abdullah Albizri, S. Dinç, Bilal Gonen","doi":"10.1080/2573234X.2020.1751569","DOIUrl":"https://doi.org/10.1080/2573234X.2020.1751569","url":null,"abstract":"ABSTRACT This study aims to develop a decision support tool for identifying the point velocity profiles in rivers. The tool enables managers to make timely and accurate decisions, thereby eliminating a substantial amount of time, cost, and effort spent on measurement procedures. In the proposed study, three machine learning classification algorithms, Artificial Neural Networks (ANN), Classification & Regression Trees (C&RT) and Tree Augmented Naïve Bayes (TAN) along with Multinomial Logistic Regression (MLR), are employed to classify the point velocities in rivers. The results showed that ANN has outperformed the other classification algorithms in predicting the outcome that was converted into 10 ordinal classes, by achieving the accuracy level of 0.46. Accordingly, a decision support tool incorporating ANN has been developed. Such a tool can be utilized by end-users (managers/practitioners) without any expertise in the machine learning field. This tool also helps in achieving success for financial investors and other relevant stakeholders.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":"15 1","pages":"67 - 78"},"PeriodicalIF":0.0,"publicationDate":"2020-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89238386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Organisational project evaluation via machine learning techniques: an exploration 通过机器学习技术进行组织项目评估:探索
Journal of Business Analytics Pub Date : 2019-07-03 DOI: 10.1080/2573234X.2019.1675478
Alon Yaakobi, M. Goresh, Iris Reychav, R. McHaney, Lin Zhu, Hanoch Sapoznikov, Yuval Lib
{"title":"Organisational project evaluation via machine learning techniques: an exploration","authors":"Alon Yaakobi, M. Goresh, Iris Reychav, R. McHaney, Lin Zhu, Hanoch Sapoznikov, Yuval Lib","doi":"10.1080/2573234X.2019.1675478","DOIUrl":"https://doi.org/10.1080/2573234X.2019.1675478","url":null,"abstract":"ABSTRACT This study explores ways an organisation can save time; review all proposed innovative, internal ideas; and, identify relevant start-up companies able to bring these ideas to fruition within a knowledge management framework. It uses text-mining techniques, including Python for data extraction and manipulation and topic modelling with Latent Dirichlet Allocation and Jaccard similarity indexes as a basis for evaluation of potentially valuable project ideas. Results show that internal organisational project ideas can be automatically matched with external data regarding potential implementation partners using big data knowledge management approaches. This ensures internal ideas are not overlooked or lost, but rather considered further so potentially profitable and viable opportunities are not missed. Increased use of big data to predict innovation and add value opens new channels to utilise text analysis in organisations and ensure internal innovation through a sustainable knowledge management approach.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":"17 1","pages":"147 - 159"},"PeriodicalIF":0.0,"publicationDate":"2019-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75553549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Bayesian assessment of predictors’ contributions to variation in the predictive performance of a logistic regression model 贝叶斯评估预测者对逻辑回归模型预测性能变化的贡献
Journal of Business Analytics Pub Date : 2019-07-03 DOI: 10.1080/2573234X.2019.1678400
Yonggang Lu
{"title":"Bayesian assessment of predictors’ contributions to variation in the predictive performance of a logistic regression model","authors":"Yonggang Lu","doi":"10.1080/2573234X.2019.1678400","DOIUrl":"https://doi.org/10.1080/2573234X.2019.1678400","url":null,"abstract":"ABSTRACT The logistic regression model is the algorithm most commonly applied in business analytics applications for classifying objects into binary categories among industrial users. This paper presents a Bayesian approach to assessing the contributions of predictors to the predictive performance of the classification model. Our proposed approach has two novel features that distinguish it from the usual approaches for such purpose. First, our approach ranks different predictors based on their contributions to variation in a model’s predictive performance, thus addressing the challenges of prediction risk and suggesting modelling strategy. Second, our approach can evaluate the contributions of every individual predictor each pair of two predictors. Hence, it can provide valuable information for managers on highly defined and detail-oriented business inquiries, complementary to the routine information conveyed by the usual methods for variable and feature selection purpose. We demonstrate the proposed approach using an example in credit risk management.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":"47 1","pages":"134 - 146"},"PeriodicalIF":0.0,"publicationDate":"2019-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80791046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
How ideological network influences terrorist attack tactics? An empirical study 意识形态网络如何影响恐怖袭击策略?实证研究
Journal of Business Analytics Pub Date : 2019-07-03 DOI: 10.1080/2573234X.2019.1678401
Lyudmyla Starostyuk, Kay-Yut Chen, E. Prater
{"title":"How ideological network influences terrorist attack tactics? An empirical study","authors":"Lyudmyla Starostyuk, Kay-Yut Chen, E. Prater","doi":"10.1080/2573234X.2019.1678401","DOIUrl":"https://doi.org/10.1080/2573234X.2019.1678401","url":null,"abstract":"ABSTRACT Global news reflects the inefficiency of the current counterterrorism strategy. The number of terrorist attacks worldwide continues to grow. Multiple studies have not yet discovered how the configuration of links among terrorists drives their choice of attack. Using “the starfish and the spider” framework, this empirical study examines both the centralised and decentralised structures of the terrorist network. We draw the network from operational tactics of different ideologies. Focusing on the types of attacks, we explore similarities in terrorist operations, discern clusters among ideological movements, and draw the structure of terrorist networks over time. The findings contribute to an improved understanding of the operational conception of violent groups. It was found that almost half of ideological movements connect through clusters with several links stable over decades. These networks transformed from a centralised (spider) hierarchy to a decentralised (starfish) structure and eventually evolved into the combination of two – a hybrid organisation.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":"32 1","pages":"101 - 117"},"PeriodicalIF":0.0,"publicationDate":"2019-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89230947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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