Publication2023
Sentiment analysis of reviews using Nature Inspired PSO and ANN
The record
- Type
- Publication
- Date
- 1 January 2023
- Level
- National
- Journal
- 2023 Global Conference on Information Technologies and Communications Gcitc 2023
- Published
- 1 January 2023
- DOI
- 10.1109/gcitc60406.2023.10426381
- Scopus ID
- 85191685378
- Authors
- 6
- Open access
- No
- Citations
- 5
- 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
Particle swarm optimization for feature selection with artificial neural network classification improves sentiment analysis on social media review data.
- 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
Nature-inspired optimization combined with neural networks represents artificial intelligence and machine learning research advancement.
- 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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- Source rows resolved into this record
- 1
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Citing this record
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KIIT Deemed to be University. “Sentiment analysis of reviews using Nature Inspired PSO and ANN”. Kortex.