Paper Title
Textual Taste Buds: A Profound Exploration of Emotion Identification in Food Recipes through BERT and AttBiRNN Models
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Authors
Keywords
Food Review, Deep Learning, Sentiment Analysis, BERT
Abstract
Sentiment analysis of food reviews has become increasingly vital in understanding customer preferences and enhancing the food and hospitality industry's offerings. However, conducting precise sentiment analysis in this context, particularly with five distinct sentiment classes (very negative, negative, neutral, positive, and very positive), poses significant challenges. This paper addresses these challenges by proposing an advanced approach for five-class sentiment analysis. Leveraging state-of-the-art natural language processing techniques, we explored various models, including RNN, BiRNN, AttBiRNN, and BERT. Our results indicate that BERT, with its bidirectional contextual understanding, outperformed the other models, achieving the highest accuracy and F1 score. AttBiRNN also demonstrated strong performance. This research contributes to the field of sentiment analysis, providing valuable insights into customer sentiments within the food and hospitality industry and emphasizing the efficacy of advanced models like BERT for fine-grained analysis.
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How To Cite (APA)
Amir Ali, Stanisław Matuszewski, Jacek Czupyt, & Usman Ahmad (October-2023). Textual Taste Buds: A Profound Exploration of Emotion Identification in Food Recipes through BERT and AttBiRNN Models. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(10), a690-a694. https://ijnrd.org/papers/IJNRD2310077.pdf
Issue
Volume 8 Issue 10, October-2023
Pages : a690-a694
Other Publication Details
Paper Reg. ID: IJNRD_206484
Published Paper Id: IJNRD2310077
Downloads: 000122254
Research Area: Computer Science & TechnologyÂ
Author Type: Foreign Author
Country: Warsaw, Mazowiecka, Poland
Published Paper PDF: https://ijnrd.org/papers/IJNRD2310077.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2310077
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