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Optimization Focused on Parallel Fuzzy Deep Belief Neural Network for Opinion Mining

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Business Intelligence (CBI 2022)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 449))

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Abstract

In this work, we propose a new parallel fuzzy deep belief neural network for sentiment analysis. We have applied several preprocessing tasks to enhance data quality and remove noisy data. Then, we have applied a semi-automatic data labeling over the dataset by combining two techniques: Vader lexicon and Mamdani’s fuzzy system. In addition, we have used four extraction techniques, which are: TFIDF (Unigram), TFIDF (Bigram), TFIDF (Trigram) and GloVe in order to represent each tweet by numerical vector. Further, we have implemented three feature selection techniques which are: The mutual information approach, the chi-square method and the ANOVA technique. Finally, we have applied the deep belief network as classifier in order to classify each tweet into a neutral, negative or positive and our hybrid parallel deep-fuzzy belief neural network is deployed in a parallel design employing the Hadoop framework to overcome the issue of long runtime of huge data sets. Also, a comparisons of the proposed model’s effectiveness with other existing models in the literature is carried out and the experimental results shown that our suggested parallel fuzzy model surpasses the baseline models by a considerable margin in terms of recall, runtime, F1 score, accuracy, error rate and precision.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Faculty of Sciences and Technology, Sultan Moulay Slimane University, 23000, Beni Mellal, Morocco

    Fatima Es-sabery, Khadija Es-sabery & Abdellatif Hair

  2. Department of Mathematics, Faculty of Sciences and Technology, Sultan Moulay Slimane University, 23000, Beni Mellal, Morocco

    Bouchra El Akraoui

Authors
  1. Fatima Es-sabery
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  2. Khadija Es-sabery
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  3. Bouchra El Akraoui
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  4. Abdellatif Hair
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Corresponding author

Correspondence to Fatima Es-sabery .

Editor information

Editors and Affiliations

  1. Sultan Moulay Slimane University, Beni-Mellal, Morocco

    Mohamed Fakir

  2. Sultan Moulay Slimane University, Beni Mellal, Morocco

    Mohamed Baslam

  3. Sultan Moulay Slimane University, Beni-Mellal, Morocco

    Rachid El Ayachi

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Es-sabery, F., Es-sabery, K., El Akraoui, B., Hair, A. (2022). Optimization Focused on Parallel Fuzzy Deep Belief Neural Network for Opinion Mining. In: Fakir, M., Baslam, M., El Ayachi, R. (eds) Business Intelligence. CBI 2022. Lecture Notes in Business Information Processing, vol 449. Springer, Cham. https://doi.org/10.1007/978-3-031-06458-6_1

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  • DOI: https://doi.org/10.1007/978-3-031-06458-6_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06457-9

  • Online ISBN: 978-3-031-06458-6

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