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Author Ruz, G.A.; Henriquez, P.A.; Mascareno, A.
Title Sentiment analysis of Twitter data during critical events through Bayesian networks classifiers Type
Year 2020 Publication Future Generation Computer Systems-The International Journal Of Escience Abbreviated Journal Futur. Gener. Comp. Syst.
Volume 106 Issue Pages 92-104
Keywords Bayesian network classifiers; Twitter data; Sentiment analysis; Bayes factor; Support vector machines; Random forests
Abstract Sentiment analysis through machine learning using Twitter data has become a popular topic in recent years. Here we address the problem of sentiment analysis during critical events such as natural disasters or social movements. We consider Bayesian network classifiers to perform sentiment analysis on two datasets in Spanish: the 2010 Chilean earthquake and the 2017 Catalan independence referendum. In order to automatically control the number of edges that are supported by the training examples in the Bayesian network classifier, we adopt a Bayes factor approach for this purpose, yielding more realistic networks. The results show the effectiveness of using the Bayes factor measure as well as its competitive predictive results when compared to support vector machines and random forests, given a sufficient number of training examples. Also, the resulting networks allow to identify the relations amongst words, offering interesting qualitative information to historically and socially comprehend the main features of the event dynamics. (C) 2020 Elsevier B.V. All rights reserved.
Address [Ruz, Gonzalo A.; Henriquez, Pablo A.] Univ Adolfo Ibanez, Fac Ingn & Ciencias, Santiago, Chile, Email: gonzalo.ruz@uai.cl;
Corporate Author Thesis
Publisher Elsevier Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0167-739x ISBN Medium
Area Expedition Conference
Notes WOS:000527320000009 Approved
Call Number UAI @ eduardo.moreno @ Serial 1145
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Author Valle, M.A.; Ruz, G.A.; Masias, V.H.
Title Using self-organizing maps to model turnover of sales agents in a call center Type
Year 2017 Publication Applied Soft Computing Abbreviated Journal Appl. Soft. Comput.
Volume 60 Issue Pages 763-774
Keywords Self organizing map; Fused network; Classifier; Employee turnover; Call center; Personality traits
Abstract This paper proposes an approach for modeling employee turnover in a call center using the versatility of supervised self-organizing maps. Two main distinct problems exist for the modeling employee turnover: first, to predict the employee turnover at a given point in the sales agent's trial period, and second to analyze the turnover behavior under different performance scenarios by using psychometric information about the sales agents. Identifying subjects susceptible to not performing well early on, or identifying personality traits in an individual that does not fit with the work style is essential to the call center industry, particularly when this industry suffers from high employee turnover rates. Self-organizing maps can model non-linear relations between different attributes and ultimately find conditions between an individual's performance and personality attributes that make him more predisposed to not remain long in an organization. Unlike other models that only consider performance attributes, this work successfully uses psychometric information that describes a sales agent's personality, which enables a better performance in predicting turnover and analyzing potential personality profiles that can identify agents with better prospects of a successful career in a call center. The application of our model is illustrated and real data are analyzed from an outbound call center. (C) 2017 Elsevier B.V. All rights reserved.
Address [Valle, Mauricio A.] Univ Finis Terrae, Fac Econ & Negocios, Santiago, Chile, Email: mvalle@uft.cL
Corporate Author Thesis
Publisher Elsevier Science Bv Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1568-4946 ISBN Medium
Area Expedition Conference
Notes WOS:000414072200057 Approved
Call Number UAI @ eduardo.moreno @ Serial 795
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Author Valle, M.A.; Varas, S.; Ruz, G.A.
Title Job performance prediction in a call center using a naive Bayes classifier Type
Year 2012 Publication Expert Systems With Applications Abbreviated Journal Expert Syst. Appl.
Volume 39 Issue 11 Pages 9939-9945
Keywords Employee turnover; Job performance; Naive Bayesian classifier; Call center
Abstract This study presents an approach to predict the performance of sales agents of a call center dedicated exclusively to sales and telemarketing activities. This approach is based on a naive Bayesian classifier. The objective is to know what levels of the attributes are indicative of individuals who perform well. A sample of 1037 sales agents was taken during the period between March and September of 2009 on campaigns related to insurance sales and service pre-paid phone services, to build the naive Bayes network. It has been shown that, socio-demographic attributes are not suitable for predicting performance. Alternatively, operational records were used to predict production of sales agents, achieving satisfactory results. In this case, the classifier training and testing is done through a stratified tenfold cross-validation. It classified the instances correctly 80.60% of times, with the proportion of false positives of 18.1% for class no (does not achieve minimum) and 20.8% for the class yes (achieves equal or above minimum acceptable). These results suggest that socio-demographic attributes has no predictive power on performance, while the operational information of the activities of the sale agent can predict the future performance of the agent. (c) 2012 Elsevier Ltd. All rights reserved.
Address [Valle, Mauricio A.] Univ Valparaiso, Fac Ciencias Econ & Adm, Santiago, Chile, Email: mauricio.valle@uv.cl
Corporate Author Thesis
Publisher Pergamon-Elsevier Science Ltd Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0957-4174 ISBN Medium
Area Expedition Conference
Notes WOS:000303300900001 Approved
Call Number UAI @ eduardo.moreno @ Serial 213
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