Text Sentiment Analysis using Different Types of Recurrent Neural Networks

This work presents the results of research aimed at comparing and analyzing Recurrent Neural Networks (RNNs), specifically LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) models to identify the effectiveness of these models in the sentiment analysis of text messages. LSTM and GRU sentim...

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Bibliographic Details
Published in2024 5th International Conference on Image Processing and Capsule Networks (ICIPCN) pp. 383 - 387
Main Authors Filimonova, Tetyana, Pursky, Oleg, Babenko, Vitalina, Nechepourenko, Andrey, Shvets, Victoria, Gamaliy, Volodymyr
Format Conference Proceeding
LanguageEnglish
Published IEEE 03.07.2024
Subjects
Accuracy
Adaptation models
Analytical models
GRU
LSTM
Recurrent neural networks
Sentiment analysis
Social networking (online)
Training
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Summary:This work presents the results of research aimed at comparing and analyzing Recurrent Neural Networks (RNNs), specifically LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) models to identify the effectiveness of these models in the sentiment analysis of text messages. LSTM and GRU sentiment analysis models have been developed. For model training, a public dataset from the social media platform Twitter, consisting of text messages and corresponding sentiment labels is used. Obtained study results allow to conclude that compared to LSTM, the GRU model has proved to be more effective specifically in the task of text classification, which resulted in higher accuracy and lower losses during training.
DOI:10.1109/ICIPCN63822.2024.00068