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Sentiment analysis using long short term memory and amended dwarf mongoose optimization algorithm.

Haisheng Deng¹, Ahmed Alkhayyat²

¹Xijing University, Xi'an, 710123, Shaanxi, China.

Scientific Reports

|May 17, 2025

Summary

This summary is machine-generated.

This study introduces an optimized Long Short-Term Memory (LSTM) model using the amended dwarf mongoose optimization (ADMO) algorithm for sentiment analysis. The proposed LSTM-ADMO model achieved high accuracy, outperforming other methods on benchmark datasets.

Keywords:

Amended dwarf mongoose optimization (ADMO) algorithm GloVe Long short-term memory (LSTM)Sentiment analysis Word2Vec

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Area of Science:

Natural Language Processing
Machine Learning
Artificial Intelligence

Background:

Sentiment analysis is crucial but challenging due to text complexities like abbreviations and errors.
Traditional methods struggle with nuanced sentiment detection in diverse text data.

Purpose of the Study:

To enhance sentiment analysis performance using advanced machine learning techniques.
To investigate the effectiveness of different word embedding models combined with an optimized LSTM architecture.

Main Methods:

Utilized GloVe and Word2Vec for text vectorization.
Employed an optimized Long Short-Term Memory (LSTM) model with the amended dwarf mongoose optimization (ADMO) algorithm for hyperparameter tuning.
Evaluated the model on the IMDB and SST-2 datasets.

Main Results:

The LSTM-ADMO model achieved high accuracy, reaching 97.84% on SST-2 with Word2Vec.
Performance differences between Word2Vec and GloVe were minimal.
The proposed model demonstrated superior performance compared to existing methods.

Conclusions:

The optimized LSTM-ADMO model is highly effective for sentiment analysis, even with complex text data.
Both Word2Vec and GloVe are suitable word embedding techniques for this approach.
The study highlights the potential of metaheuristic optimization for deep learning models in NLP.