INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15
Interactive online environments have become crucial outlets for expressing opinions and sentiments. Sentiment analysis, categorizing thoughts as positive, negative, or neutral, aids in areas such as marketing and behavior analysis. Often, sentiments are conveyed with sarcasm, posing a challenge for conventional sentiment classifiers. Current research treats sentiment and sarcasm as distinct tasks, but our work suggests they are interconnected. We propose a deep neural networks to improve sentiment analysis by considering the correlation with sarcasm. Our approach outperforms existing methods, achieving a 94 percent F1-score, highlighting the effectiveness of jointly addressing sentiment and sarcasm.
"Sentiment Analysis and Sarcasm Detection using Deep Learning", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.9, Issue 4, page no.909 - 916, April-2024, Available :http://www.ijsdr.org/papers/IJSDR2404130.pdf
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Publication Details:
Published Paper ID: IJSDR2404130
Registration ID:210394
Published In: Volume 9 Issue 4, April-2024
DOI (Digital Object Identifier):
Page No: 909 - 916
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631
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