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url = {https://www.sciencedirect.com/science/article/pii/S0378778822001487},
author = {Natalia Giraldo Vasquez and Cláudia Naves David Amorim and Barbara Matusiak and Julia Kanno and Natalia Sokol and Justyna Martyniuk-Peczek and Sergio Sibilio and Michelangelo Scorpio and Yasuko Koga},

}







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pages = {106-110},
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doi = {https://doi.org/10.1016/j.foodqual.2017.07.002},
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author = {Jun Niimi , Minyu Liu , Susan E.P. Bastian and Susan E.P. Bastian}

}



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@Article{doi:10.1177/0034355219847284,
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  year     = {2020},
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  volume   = {63},
  doi      = {10.1177/0034355219847284},
  url      = {https://doi.org/10.1177/0034355219847284}
}


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title = {Social emotion classification of short text via topic-level maximum entropy model},
journal = {Information \& Management},
volume = {53},
number = {8},
pages = {978-986},
year = {2016},
note = {Big Data Commerce},
issn = {0378-7206},
doi = {https://doi.org/10.1016/j.im.2016.04.005},
url = {https://www.sciencedirect.com/science/article/pii/S0378720616300386},
author = {Yanghui Rao and Haoran Xie and Jun Li and Fengmei Jin and Fu Lee Wang and Qing Li}
}


@Article{app9061123,
AUTHOR = {Jabreel, Mohammed and Moreno, Antonio},
TITLE = {A Deep Learning-Based Approach for Multi-Label Emotion Classification in Tweets},
JOURNAL = {Applied Sciences},
VOLUME = {9},
YEAR = {2019},
NUMBER = {6},
ARTICLE-NUMBER = {1123},
URL = {https://www.mdpi.com/2076-3417/9/6/1123},
ISSN = {2076-3417},
DOI = {10.3390/app9061123}
}



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title = {Deep learning for affective computing: Text-based emotion recognition in decision support},
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pages = {24-35},
year = {2018},
issn = {0167-9236},
doi = {https://doi.org/10.1016/j.dss.2018.09.002},
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author = {Bernhard Kratzwald and Suzana Ilić and Mathias Kraus and Stefan Feuerriegel and Helmut Prendinger}

}



@Article{su132414009,
AUTHOR = {Ramos-Hidalgo, Encarnación and Orta-Pérez, Manuel and Agustí, Maria A.},
TITLE = {Ethics and Social Responsibility in Family Firms. Research Domain and Future Research Trends from a Bibliometric Perspective},
JOURNAL = {Sustainability},
VOLUME = {13},
YEAR = {2021},
NUMBER = {24},
ARTICLE-NUMBER = {14009},
URL = {https://www.mdpi.com/2071-1050/13/24/14009},
ISSN = {2071-1050},
DOI = {10.3390/su132414009}
}

@article{10.1162/NECO_a_00849,
    author = {Guliyev, Namig J. and Ismailov, Vugar E.},
    title = "{A Single Hidden Layer Feedforward Network with Only One Neuron in the Hidden Layer Can Approximate Any Univariate Function}",
    journal = {Neural Computation},
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    number = {7},
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    year = {2016},
    month = {07},
    abstract = "{The possibility of approximating a continuous function on a compact subset of the real line by a feedforward single hidden layer neural network with a sigmoidal activation function has been studied in many papers. Such networks can approximate an arbitrary continuous function provided that an unlimited number of neurons in a hidden layer is permitted. In this note, we consider constructive approximation on any finite interval of  by neural networks with only one neuron in the hidden layer. We construct algorithmically a smooth, sigmoidal, almost monotone activation function  providing approximation to an arbitrary continuous function within any degree of accuracy. This algorithm is implemented in a computer program, which computes the value of  at any reasonable point of the real axis.}",
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    doi = {10.1162/NECO_a_00849},
    url = {https://doi.org/10.1162/NECO\_a\_00849},
    eprint = {https://direct.mit.edu/neco/article-pdf/28/7/1289/974567/neco\_a\_00849.pdf},
}

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author = {V. A. F. Costa},
title = {On the convergence of the iterative methods},
journal = {Numerical Heat Transfer, Part B: Fundamentals},
volume = {81},
number = {1-6},
pages = {1-13},
year  = {2022},
publisher = {Taylor \& Francis},
doi = {10.1080/10407790.2021.1997029},

URL = { https://doi.org/10.1080/10407790.2021.1997029},

}
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journal = {Linear Algebra and its Applications},
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@Comment{jabref-meta: databaseType:bibtex;}
