Emotion processing by applying a fuzzy-based vader lexicon and a parallel deep belief network over massive data

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dc.contributor.authorEs-sabery, Fatima
dc.contributor.authorEs-sabery, Ibrahim
dc.contributor.authorHair, Abdellatif
dc.contributor.authorSainz de Abajo, Beatriz
dc.contributor.authorGarcía-Zapirain, Begoña
dc.date.accessioned2025-05-26T09:33:33Z
dc.date.available2025-05-26T09:33:33Z
dc.date.issued2022-08-26
dc.date.updated2025-05-26T09:33:33Z
dc.description.abstractEmotion processing has been a very intense domain of investigation in data analysis and NLP during the previous few years. Currently, the algorithms of the deep neural networks have been applied for opinion mining tasks with good results. Among various neuronal models applied for opinion mining a deep belief network (DBN) model has gained more attention. In this proposal, we have developed a combined classifier based on fuzzy Vader lexicon and a parallel deep belief network for emotion analysis. We have implemented multiple pretreatment techniques to improve the quality and soundness of the data and eliminate disturbing data. Afterward, we have performed a semi-automatic dataset labeling using a combination of two different methods: Mamdani's fuzzy system and Vader lexicon. As well, we have applied four feature extractors, which are: GloVe, TFIDF (Trigram), TFIDF (Bigram), TFIDF (Unigram) with the aim of transforming each incoming tweet into a digital value vector. In addition, we have integrated three feature selectors, namely: The ANOVA method, the chi-square approach and the mutual information technique with the objective of selecting the most relevant features. Further, we have implemented the DBN as classifier for classifying each inputted tweet into three categories: neutral, positive or negative. At the end, we have deployed our proposed approach in parallel way employing both Hadoop and Spark framework with the purpose of overcoming the problem of long runtime of massive data. Furthermore, we have carried out a comparison between our newly suggested hybrid approach and alternative hybrid models available in the literature. From the experimental findings, it was found that our suggested vague parallel approach is more powerful than the baseline patterns in terms of false negative rate (1.33%), recall (99.75%), runtime (32.95s), convergence, stability, F1 score (99.53%), accuracy (98.96%), error rate (1.04%), kappa-Static (99.1%), complexity, false positive rate (0.25%), precision rate (97.59%) and specificity rate (98.67%). As a conclusion, our vague parallel approach outperforms baseline and deep learning models, as well as certain other approaches chosen from the literature.en
dc.description.sponsorshipeVida Research Group, University of Deusto, Bilbao, Spain (Grant Number: IT 905-16)en
dc.identifier.citationEs-Sabery, F., Es-Sabery, I., Hair, A., Sainz-De-Abajo, B., & Garcia-Zapirain, B. (2022). Emotion processing by applying a fuzzy-based vader lexicon and a parallel deep belief network over massive data. IEEE Access, 10, 87870-87899. https://doi.org/10.1109/ACCESS.2022.3200389
dc.identifier.doi10.1109/ACCESS.2022.3200389
dc.identifier.eissn2169-3536
dc.identifier.urihttp://hdl.handle.net/20.500.14454/2828
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.rights© 2025 IEEE
dc.subject.otherDeep belief neural network
dc.subject.otherExtractors of features
dc.subject.otherFuzzy logic
dc.subject.otherHadoop
dc.subject.otherHDFS
dc.subject.otherMapReduce
dc.subject.otherSelectors of features
dc.subject.otherSentiment analysis
dc.titleEmotion processing by applying a fuzzy-based vader lexicon and a parallel deep belief network over massive dataen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.endPage87899
oaire.citation.startPage87870
oaire.citation.titleIEEE Access
oaire.citation.volume10
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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