Publication2024
Multi-label Aspect Based Sentiment Analysis using Fuzzy LSTM
The record
- Type
- Publication
- Date
- 1 January 2024
- Level
- National
- Journal
- 2024 IEEE 1st International Conference on Green Industrial Electronics and Sustainable Technologies Giest 2024
- Published
- 1 January 2024
- DOI
- 10.1109/giest62955.2024.10960135
- Scopus ID
- 105004558267
- Authors
- 4
- Open access
- No
- Citations
- 0
- International co-authorship
- No
- Co-author countries
- IN
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
LSTM-based sentiment analysis extends natural language processing research for understanding customer reviews and emotions.
- Moderate confidence
- The alignment is plausible and defensible but rests on an assumption a reasonable reviewer could decline. Published, and surfaced in the review queue for a human to confirm or override.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-3.2 Digital technology and artificial intelligence
Fuzzy logic and deep learning combination advances artificial intelligence capability for multi-scale sentiment classification.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
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- 1
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Citing this record
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KIIT Deemed to be University. “Multi-label Aspect Based Sentiment Analysis using Fuzzy LSTM”. Kortex.