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Open AccessArticle

Ensemble Deep Learning for Multilabel Binary Classification of User-Generated Content

1
School of Computer Science, University of Nottingham, Nottingham NG8 1BB, UK
2
Department of Computer Science and Biomedical Informatics, University of Thessaly, 35131 Lamia, Greece
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Author to whom correspondence should be addressed.
Algorithms 2020, 13(4), 83; https://doi.org/10.3390/a13040083
Received: 14 February 2020 / Revised: 29 March 2020 / Accepted: 30 March 2020 / Published: 1 April 2020
(This article belongs to the Special Issue Ensemble Algorithms and Their Applications)
Sentiment analysis usually refers to the analysis of human-generated content via a polarity filter. Affective computing deals with the exact emotions conveyed through information. Emotional information most frequently cannot be accurately described by a single emotion class. Multilabel classifiers can categorize human-generated content in multiple emotional classes. Ensemble learning can improve the statistical, computational and representation aspects of such classifiers. We present a baseline stacked ensemble and propose a weighted ensemble. Our proposed weighted ensemble can use multiple classifiers to improve classification results without hyperparameter tuning or data overfitting. We evaluate our ensemble models with two datasets. The first dataset is from Semeval2018-Task 1 and contains almost 7000 Tweets, labeled with 11 sentiment classes. The second dataset is the Toxic Comment Dataset with more than 150,000 comments, labeled with six different levels of abuse or harassment. Our results suggest that ensemble learning improves classification results by 1.5 % to 5.4 % . View Full-Text
Keywords: ensemble learning; sentiment analysis; multilabel classification; deep neural networks; pure emotion; Semeval 2018 Task 1; toxic comment classification ensemble learning; sentiment analysis; multilabel classification; deep neural networks; pure emotion; Semeval 2018 Task 1; toxic comment classification
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MDPI and ACS Style

Haralabopoulos, G.; Anagnostopoulos, I.; McAuley, D. Ensemble Deep Learning for Multilabel Binary Classification of User-Generated Content. Algorithms 2020, 13, 83. https://doi.org/10.3390/a13040083

AMA Style

Haralabopoulos G, Anagnostopoulos I, McAuley D. Ensemble Deep Learning for Multilabel Binary Classification of User-Generated Content. Algorithms. 2020; 13(4):83. https://doi.org/10.3390/a13040083

Chicago/Turabian Style

Haralabopoulos, Giannis; Anagnostopoulos, Ioannis; McAuley, Derek. 2020. "Ensemble Deep Learning for Multilabel Binary Classification of User-Generated Content" Algorithms 13, no. 4: 83. https://doi.org/10.3390/a13040083

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