Efficient Deep learning Improvised attention-based approach for aspect-based sentiment analysis

Registration ID: IJNRD_187838

Published ID: IJNRD2304069

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Madhumita

Sentiment Analysis, OSADL, IADL

Statistics show that nearly one-third of people who use social media frequently use Twitter and 75% of them express their opinions. Social media are used for information sharing. Twitter is a social media site where users can read, post messages known as "tweets," interact with various communities, and express their opinions. Through sentiment analysis, these opinions can be useful for a number of applications, most notably for business growth. Further issues with sentiment analysis in twitter include taking into account emotions, rumours, emoji, and other things; this makes sentiment analysis of the twitter data quite difficult. Therefore, using the OSADL (Optimized Self-attention Deep Learning) framework, we proposed aspect-based sentiment analysis in this paper. Additionally, this research focuses on taking into account two distinctive features, namely Context feature and sense feature, which are disregarded by the existing model. Additionally, we extract these two features using the IADL (Improvised Attention-based Deep Learning) framework, and then we combine them for greater accuracy. Real Twitter data from SamEval 2014 Task 4 is used to evaluate the proposed model. It is then further evaluated by performing a comparison analysis with existing methodologies using various metrics, including precision, recall, accuracy, and macro-F1.

Published Paper   E-Certificate

Madhumita (April-2023). Efficient Deep learning Improvised attention-based approach for aspect-based sentiment analysis. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(4), a562-a570. https://ijnrd.org/papers/IJNRD2304069.pdf

Volume 8 Issue 4, April-2023

Pages : a562-a570

Paper Reg. ID: IJNRD_187838

Published Paper Id: IJNRD2304069

Research Area: Science & Technology

Author Type: Indian Author

Country: Bangalore, Karnataka, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2304069.pdf

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