Research and Analysis of Deep Learning Models for Emotion Analysis Tasks

Mingcheng Yang

doi:10.54254/2755-2721/2026.TJ29617

Applied and Computational EngineeringOpen access

Research and Analysis of Deep Learning Models for Emotion Analysis Tasks

Research Article

Open Access

Research and Analysis of Deep Learning Models for Emotion Analysis Tasks

Mingcheng Yang ^1*

¹ School of Computer Science and Software, Southwest Petroleum University, Chengdu, China

^*Corresponding author: 202331061227@stu.swpu.edu.cn

Published on 19 November 2025

ACE Vol.207

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-539-4

ISBN (Online): 978-1-80590-540-0

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Abstract

The field of Natural Language Processing (NLP) currently has a bright future, but it still faces challenges due to a series of issues such as language complexity, data resources and limitations. Therefore, this paper starts with the classic sentiment analysis problem. Based on the IMDB movie review dataset, by evaluating the original dataset, the semantic contradiction set obtained by filtering the original data, and the easily confused dataset obtained by training the large prediction model with prompt words, this paper systematically estimates the basic performance of a series of models including CNN, LSTM, BiLSTM, GRU, MLP, Attention, Multi-HeadAttention, Transformer, and PositionalEmbedding+Transformer. With this in-depth study, the basic generalization ability, anti-interference ability, and fine-grained semantic understanding ability of the model are studied. Comparison of the original test set and the semantically contradictory set shows that each model has excellent basic generalization capabilities. The GRU demonstrates the strongest interference resistance in the semantically contradictory set, while the LSTM demonstrates the best fine-grained semantic understanding in the easily confused set. Combining the scores of indicators across the test sets, the Attention model demonstrates the most comprehensive overall performance. This research reflects the potential for further development of RNN and its variants and suggests the possibility and potential of models such as the RNN+Attention model.

Keywords:

Sentiment Analysis, IMDB Dataset, Deep Learning, Attention Mechanism

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