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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Efficient Deep learning Improvised attention-based approach for aspect-based sentiment analysis |
| Authors Name: | Madhumita |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_187838
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| Published Paper Id: | IJNRD2304069 |
| Published In: | Volume 8 Issue 4, April-2023 |
| DOI: | |
| Abstract: | 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. |
| Keywords: | Sentiment Analysis, OSADL, IADL |
| Cite Article: | "Efficient Deep learning Improvised attention-based approach for aspect-based sentiment analysis", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 4, page no.a562-a570, April-2023, Available :http://www.ijnrd.org/papers/IJNRD2304069.pdf |
| Downloads: | 000118751 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID:IJNRD2304069 Registration ID: 187838 Published In: Volume 8 Issue 4, April-2023 DOI (Digital Object Identifier): Page No: a562-a570 Country: Bangalore, Karnataka, India Research Area: Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2304069 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2304069 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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