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A Survey on Deep Learning in Image Polarity Detection: Balancing Generalization Performances and Computational Costs

Electronics (Basel), 2019-07, Vol.8 (7), p.783 [Peer Reviewed Journal]

2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2079-9292 ;EISSN: 2079-9292 ;DOI: 10.3390/electronics8070783

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  • Title:
    A Survey on Deep Learning in Image Polarity Detection: Balancing Generalization Performances and Computational Costs
  • Author: Ragusa, Edoardo ; Cambria, Erik ; Zunino, Rodolfo ; Gastaldo, Paolo
  • Subjects: Artificial neural networks ; Automation ; Classification ; Complexity ; Computing costs ; computing resources ; convolutional neural networks ; Deep learning ; Design ; Feature extraction ; image polarity detection ; Machine learning ; Performance evaluation ; Sentiment analysis ; Social networks ; transfer learning
  • Is Part Of: Electronics (Basel), 2019-07, Vol.8 (7), p.783
  • Description: Deep convolutional neural networks (CNNs) provide an effective tool to extract complex information from images. In the area of image polarity detection, CNNs are customarily utilized in combination with transfer learning techniques to tackle a major problem: the unavailability of large sets of labeled data. Thus, polarity predictors in general exploit a pre-trained CNN as the feature extractor that in turn feeds a classification unit. While the latter unit is trained from scratch, the pre-trained CNN is subject to fine-tuning. As a result, the specific CNN architecture employed as the feature extractor strongly affects the overall performance of the model. This paper analyses state-of-the-art literature on image polarity detection and identifies the most reliable CNN architectures. Moreover, the paper provides an experimental protocol that should allow assessing the role played by the baseline architecture in the polarity detection task. Performance is evaluated in terms of both generalization abilities and computational complexity. The latter attribute becomes critical as polarity predictors, in the era of social networks, might need to be updated within hours or even minutes. In this regard, the paper gives practical hints on the advantages and disadvantages of the examined architectures both in terms of generalization and computational cost.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 2079-9292
    EISSN: 2079-9292
    DOI: 10.3390/electronics8070783
  • Source: ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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