Sentiment Analysis Using LSTM and Transformers

This paper presents an analysis of student sentiment using LSTM and Transformer models. We explore the accuracy and efficiency of these models in detecting sentiment trends from student feedback datasets.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-11-24
DOI
https://doi.org/10.5281/zenodo.17702123
Primary Topic
Sentiment Analysis and Opinion Mining
Type
article
Field-Weighted Citation Impact
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article

Sentiment Analysis Using LSTM and Transformers

Zenodo (CERN European Organization for Nuclear Research)
Sentiment Analysis and Opinion Mining
article

Sentiment Analysis Using LSTM and Transformers

article en

Abstract

This paper presents an analysis of student sentiment using LSTM and Transformer models. We explore the accuracy and efficiency of these models in detecting sentiment trends from student feedback datasets.

Zenodo (CERN European Organization for Nuclear Research)
Ala-Too International University (KG), International University (KH)
Openalex Percentile: Top 22%
Sentiment Analysis and Opinion Mining
DOIOpen Access PDFOpenAlex
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Sentiment Analysis Using LSTM and Transformers · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS