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by IQAC KIITKIIT Deemed to be University — Internal Quality Assurance Cell

Publication2026

Benchmarking Traditional and Deep Sequential Models for Text Sentiment Analysis

The record

Type
Publication
Date
1 January 2026
Level
National
Journal
Esic 2026 Proceedings 6th International Conference on Emerging Systems and Intelligent Computing
Pages
810-815
Published
1 January 2026
DOI
10.1109/esic68176.2026.11495807
Scopus ID
105040788391
Authors
4
Open access
No
Citations
0
International co-authorship
No
Co-author countries
IN

Classification

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UN Sustainable Development Goals

  1. 9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending

    TargetPrimaryHigh confidenceJudged

    Benchmarks traditional and deep learning sequential models for sentiment analysis comparing classical models with neural network and attention-based architectures.

    High confidence
    The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
    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

  1. vb-3.2 Digital technology and artificial intelligence

    Sub-themePrimaryHigh confidenceJudged

    Advanced machine learning research on sentiment analysis and natural language processing contributes to AI innovation.

    Produced by
    claude-haiku-4-5-20251001, under kortex-publication-v1

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KIIT Deemed to be University. “Benchmarking Traditional and Deep Sequential Models for Text Sentiment Analysis”. Kortex.

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Benchmarking Traditional and Deep Sequential Models for Text Sentiment Analysis · Kortex