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

The use of machine learning to analyze sentiments has attained considerable interest in the past few years. The task of analyzing sentiments has becfigome increasingly important and challenging. Due to the specific attributes of this type of data, including length of text, spelling errors, and abbre...

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Veröffentlicht in:Scientific reports Jg. 15; H. 1; S. 17206 - 19
Hauptverfasser: Deng, Haisheng, Alkhayyat, Ahmed
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 17.05.2025
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Algorithms
Amended dwarf mongoose optimization (ADMO) algorithm
Datasets
Embedding
GloVe
Helogale parvula
Humanities and Social Sciences
Long short-term memory
Long short-term memory (LSTM)
Machine learning
multidisciplinary
Neural networks
Science
Science (multidisciplinary)
Sentiment analysis
Social networks
Support vector machines
Word2Vec
Sentiment analysis
GloVe
Amended dwarf mongoose optimization (ADMO) algorithm
Long short-term memory (LSTM)
Word2Vec
ISSN:2045-2322, 2045-2322
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Zusammenfassung:The use of machine learning to analyze sentiments has attained considerable interest in the past few years. The task of analyzing sentiments has becfigome increasingly important and challenging. Due to the specific attributes of this type of data, including length of text, spelling errors, and abbreviations, unconventional methods and multiple steps are required for effectively analyzing sentiment in such a complex environment. In this research, two distinct word embedding models, GloVe and Word2Vec, were utilized for vectorization. To enhance the performance long short-term memory (LSTM), the model was optimized using the amended dwarf mongoose optimization (ADMO) algorithm, leading to improvements in the hyperparameters. The LSTM–ADMO achieved the accuracy values of 97.74 and 97.47 using Word2Vec and GloVe, respectively on IMDB, and it could gain the accuracy values of 97.84 and 97.51 using Word2Vec and GloVe, respectively on SST-2. In general, it was determined that the proposed model significantly outperformed other models, and there was very little difference between the two different word embedding techniques.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-01834-1