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Author Ruz, G.A.; Henriquez, P.A.; Mascareno, A. doi  openurl
  Title Sentiment analysis of Twitter data during critical events through Bayesian networks classifiers Type Journal Article
  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 no  
  Call Number UAI @ eduardo.moreno @ Serial 1145  
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Author Alejo, L.; Atkinson, J.; Guzman-Fierro, V.; Roeckel, M. pdf  doi
openurl 
  Title Effluent composition prediction of a two-stage anaerobic digestion process: machine learning and stoichiometry techniques Type Journal Article
  Year 2018 Publication Environmental Science And Pollution Research Abbreviated Journal Environ. Sci. Pollut. Res.  
  Volume 25 Issue 21 Pages 21149-21163  
  Keywords Anaerobic digestion; Protein degradation; Machine learning; Prediction methods; Support vector machines  
  Abstract Computational self-adapting methods (Support Vector Machines, SVM) are compared with an analytical method in effluent composition prediction of a two-stage anaerobic digestion (AD) process. Experimental data for the AD of poultry manure were used. The analytical method considers the protein as the only source of ammonia production in AD after degradation. Total ammonia nitrogen (TAN), total solids (TS), chemical oxygen demand (COD), and total volatile solids (TVS) were measured in the influent and effluent of the process. The TAN concentration in the effluent was predicted, this being the most inhibiting and polluting compound in AD. Despite the limited data available, the SVM-based model outperformed the analytical method for the TAN prediction, achieving a relative average error of 15.2% against 43% for the analytical method. Moreover, SVM showed higher prediction accuracy in comparison with Artificial Neural Networks. This result reveals the future promise of SVM for prediction in non-linear and dynamic AD processes.  
  Address [Alejo, Luz; Guzman-Fierro, Victor; Roeckel, Marlene] Univ Concepcion, Dept Ingn Quim, Victor Lamas 1290,Casilla 160-C Correo 3., Concepcion, Chile, Email: john.atkinson@uai.cl;  
  Corporate Author Thesis  
  Publisher Springer Heidelberg Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0944-1344 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000438830900080 Approved no  
  Call Number UAI @ eduardo.moreno @ Serial 890  
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