Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/87262
Title: Bayesian network based extreme learning machine for subjectivity detection
Authors: Chaturvedi, Iti
Ragusa, Edoardo
Gastaldo, Paolo
Zunino, Rodolfo
Cambria, Erik
Keywords: Extreme Learning Machine
Subjectivity Detection
Issue Date: 2017
Source: Chaturvedi, I., Ragusa, E., Gastaldo, P., Zunino, R., & Cambria, E. (2017). Bayesian network based extreme learning machine for subjectivity detection. Journal of the Franklin Institute, 355(4), 1780-1797.
Series/Report no.: Journal of the Franklin Institute
Abstract: 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.
URI: https://hdl.handle.net/10356/87262
http://hdl.handle.net/10220/44342
ISSN: 0016-0032
DOI: http://dx.doi.org/10.1016/j.jfranklin.2017.06.007
Rights: © 2017 The Franklin Institute (published by Elsevier). This is the author created version of a work that has been peer reviewed and accepted for publication in Journal of the Franklin Institute, published by Elsevier on behalf of The Franklin Institute. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document.  The published version is available at: [http://dx.doi.org/10.1016/j.jfranklin.2017.06.007].
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