Bayesian network based extreme learning machine for subjectivity detection

Chaturvedi, Iti, Ragusa, Edoardo, Gastaldo, Paolo, Zunino, Rodolfo, and Cambria, Erik (2018) Bayesian network based extreme learning machine for subjectivity detection. Journal of The Franklin Institute, 355 (4). pp. 1780-1797.

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Subjectivity detection is a task of natural language processing that aims to remove ‘factual’ or ‘neutral’ content, i.e., objective text that does not contain any opinion, from online product reviews. Such a pre-processing step is crucial to increase the accuracy of sentiment analysis systems, as these are usually optimized for the binary classification task of distinguishing between positive and negative content. In this paper, we extend the extreme learning machine (ELM) paradigm to a novel framework that exploits the features of both Bayesian networks and fuzzy recurrent neural networks to perform subjectivity detection. In particular, Bayesian networks are used to build a network of connections among the hidden neurons of the conventional ELM configuration in order to capture dependencies in high-dimensional data. Next, a fuzzy recurrent neural network inherits the overall structure generated by the Bayesian networks to model temporal features in the predictor. Experimental results confirmed the ability of the proposed framework to deal with standard subjectivity detection problems and also proved its capacity to address portability across languages in translation tasks.

Item ID: 63232
Item Type: Article (Research - C1)
ISSN: 1879-2693
Keywords: Extreme Learning, Sentiment Classification, Natural Language Processing, Fuzzy Logic
Copyright Information: © 2017. The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
Date Deposited: 16 Jul 2020 02:01
FoR Codes: 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461104 Neural networks @ 50%
46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460208 Natural language processing @ 50%
SEO Codes: 90 COMMERCIAL SERVICES AND TOURISM > 9003 Tourism > 900301 Economic Issues in Tourism @ 30%
97 EXPANDING KNOWLEDGE > 970108 Expanding Knowledge in the Information and Computing Sciences @ 70%
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