Authors :
Mohammed Afthab N; Dhanush B; Sri Vignesh C; P. Deepika, M.E.
Volume/Issue :
Volume 8 - 2023, Issue 11 - November
Google Scholar :
https://tinyurl.com/yts7dext
Scribd :
https://tinyurl.com/23dz4x69
DOI :
https://doi.org/10.5281/zenodo.10209809
Abstract :
In the area of sentiment analysis using deep
learning models, this study attempts to thoroughly
analyze and assess three deep learning models: a
Feedforward Deep Neural Network (DNN), a Gated
Recurrent Unit (GRU), and Long Short-Term Memory
(LSTM). The goal is to determine which model performs
best, offering insightful information for choosing models
for sentiment analysis tasks and their useful
implementation in real-world scenarios. This work also
advances the field of sentiment analysis and natural
language processing (NLP) by providing methodological
insights into the selection of deep learning models and
evaluating their capacity for generalization.
Keywords :
Deep learning Models, Long Short Term Memory(LSTM), Sentiment Analysis, DNN,GRU, Accuracy, Efficiency.
In the area of sentiment analysis using deep
learning models, this study attempts to thoroughly
analyze and assess three deep learning models: a
Feedforward Deep Neural Network (DNN), a Gated
Recurrent Unit (GRU), and Long Short-Term Memory
(LSTM). The goal is to determine which model performs
best, offering insightful information for choosing models
for sentiment analysis tasks and their useful
implementation in real-world scenarios. This work also
advances the field of sentiment analysis and natural
language processing (NLP) by providing methodological
insights into the selection of deep learning models and
evaluating their capacity for generalization.
Keywords :
Deep learning Models, Long Short Term Memory(LSTM), Sentiment Analysis, DNN,GRU, Accuracy, Efficiency.
