Critical analysis of Big Data challenges and analytical methods
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In this article, the authors present a state-of-the-art review that presents a holistic view of the BD challenges and BDA methods theorized/proposed/employed by organizations to help others understand this landscape with the objective of making robust investment decisions.About:
This article is published in Journal of Business Research.The article was published on 2017-01-01 and is currently open access. It has received 1267 citations till now. The article focuses on the topics: Analytics & Big data.read more
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Using artificial intelligence methods to assess academic achievement in public high schools of a European Union country.
Frederico Cruz-Jesus,Mauro Castelli,Tiago Oliveira,Ricardo Manuel Nogueira Mendes,Catarina S. Nunes,Mafalda Sa-Velho,Ana Rosa-Louro +6 more
TL;DR: A novel approach is presented, using state-of-the-art artificial intelligence (AI) techniques to predict the academic achievement of virtually every public high school student in Portugal, i.e., 110,627 students in the academic year of 2014/2015.
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Profiting from big data analytics: The moderating roles of industry concentration and firm size
TL;DR: In this paper, the authors investigate the moderating roles of firm size and industry concentration in the relationship between BDA solutions and firm profitability and provide empirical evidence on the business value of BDA as well as the essential role played by contextual conditions that managers should consider.
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BERT: a sentiment analysis odyssey
Shivaji Alaparthi,Manit Mishra +1 more
TL;DR: In this paper, the authors investigated relative effectiveness of four sentiment analysis techniques: (1) unsupervised lexicon-based model using SentiWordNet, (2) traditional supervised machine learning model using logistic regression, (3) supervised deep learning models using Long Short-Term Memory (LSTM), and (4) advanced supervised DNN model using Bidirectional Encoder Representations from Transformers (BERT).
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Research on Sentiment Classification of Online Travel Review Text
TL;DR: This study transformed sentiment analysis into a multi-classification problem based on machine learning methods, and further designed a keyword semantic expansion method based on a knowledge graph to build an effective sentiment classification model for online travel review text.
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Smart e-commerce systems: current status and research challenges
TL;DR: The holistic architecture of these systems is described and the main enablers underlying the development of SESs in terms of internet of things, social media, mobile internet, big data analytics and cloud computing are analyzed.
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