 A Deep Neural Network Model for the Detection and Classification of Emotions from Textual ContentMuhammad Zubair Asghar, Adidah Lajis, Muhammad Mansoor Alam, Mohd Khairil Rahmat, Haidawati Mohamad Nasir, Hussain Ahmad, Mabrook S. Al-Rakhami, Atif Al-Amri, Fahad R. Albogamy and Muhammad Ahmad Complexity, 2022, vol. 2022, 1-12 Abstract: Emotion-based sentimental analysis has recently received a lot of interest, with an emphasis on automated identification of user behavior, such as emotional expressions, based on online social media texts. However, the majority of the prior attempts are based on traditional procedures that are insufficient to provide promising outcomes. In this study, we categorize emotional sentiments by recognizing them in the text. For that purpose, we present a deep learning model, bidirectional long-term short-term memory (BiLSMT), for emotion recognition that takes into account five main emotions (Joy, Sadness, Fear, Shame, Guilt). We use our experimental assessments on the emotion dataset to accomplish the emotion categorization job. The datasets were evaluated and the findings revealed that, when compared to state-of-the-art methodologies, the proposed model can successfully categorize user emotions into several classifications. Finally, we assess the efficacy of our strategy using statistical analysis. This research’s findings help firms to apply best practices in the selection, management, and optimization of policies, services, and product information. Date: 2022 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:8221121 DOI: 10.1155/2022/8221121 Access Statistics for this article More articles in Complexity from Hindawi |
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