Work

Sentiment Analysis Using LSTM and Transformers

Year: 2025

Type: article

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.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.17702122

Author: Iusupova, Jasmin

Institution: Ala-Too International University

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7106541487

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