@Article{crph1249,
AUTHOR = {Varadharajan, Vivek and Smith, Nathan and Kalla, Dinesh and Samaah, Fnu and Mandala, Vishwanadham},
TITLE = {Deep Learning-Based Sentiment Analysis: Enhancing IMDb Review Classification with LSTM Models},
JOURNAL = {Current Research in Public Health},
VOLUME = {4},
YEAR = {2025},
NUMBER = {1},
PAGES = {1-14},
URL = {https://www.scipublications.com/journal/index.php/UJCSC/article/view/1249},
ISSN = {2831-5162},
DOI = {10.31586/ujcsc.2025.1249},
ABSTRACT = {Sentiment analysis, a vital aspect of natural language processing, involves the application of machine learning models to discern the emotional tone conveyed in textual data. The use case for this type of problem is where businesses can make informed decisions based on customer feedback, identify the sentiments of their employees, and make decisions on hiring or retention, or for that matter, classify a text based on its topic like whether it is about a particular subject like physics or chemistry as is useful in search engines. The model leverages a sequential architecture, transforms words into dense vectors using an Embedding layer, and captures intricate sequential patterns with two Long Short-Term Memory (LSTM) layers. This model aims to effectively classify sentiments in text data using a 50-dimensional embedding dimension and 20 % dropout layers. The use of rectified linear unit (ReLU) activations enhances non-linearity, while the SoftMax activation in the output layer aligns with the multi-class nature of sentiment analysis. Both training and test accuracy were well over 80%.},
}

%0 Journal Article
%A Varadharajan, Vivek
%A Smith, Nathan
%A Kalla, Dinesh
%A Samaah, Fnu
%A Mandala, Vishwanadham
%D 2025
%J Current Research in Public Health

%@ 2831-5162
%V 4
%N 1
%P 1-14

%T Deep Learning-Based Sentiment Analysis: Enhancing IMDb Review Classification with LSTM Models
%M doi:10.31586/ujcsc.2025.1249
%U https://www.scipublications.com/journal/index.php/UJCSC/article/view/1249

TY  - JOUR
AU  - Varadharajan, Vivek
AU  - Smith, Nathan
AU  - Kalla, Dinesh
AU  - Samaah, Fnu
AU  - Mandala, Vishwanadham
TI  - Deep Learning-Based Sentiment Analysis: Enhancing IMDb Review Classification with LSTM Models
T2  - Current Research in Public Health
PY  - 2025
VL  - 4
IS  - 1
SN  - 2831-5162
SP  - 1
EP  - 14
UR  - https://www.scipublications.com/journal/index.php/UJCSC/article/view/1249
AB  - Sentiment analysis, a vital aspect of natural language processing, involves the application of machine learning models to discern the emotional tone conveyed in textual data. The use case for this type of problem is where businesses can make informed decisions based on customer feedback, identify the sentiments of their employees, and make decisions on hiring or retention, or for that matter, classify a text based on its topic like whether it is about a particular subject like physics or chemistry as is useful in search engines. The model leverages a sequential architecture, transforms words into dense vectors using an Embedding layer, and captures intricate sequential patterns with two Long Short-Term Memory (LSTM) layers. This model aims to effectively classify sentiments in text data using a 50-dimensional embedding dimension and 20 % dropout layers. The use of rectified linear unit (ReLU) activations enhances non-linearity, while the SoftMax activation in the output layer aligns with the multi-class nature of sentiment analysis. Both training and test accuracy were well over 80%.
DO  - Deep Learning-Based Sentiment Analysis: Enhancing IMDb Review Classification with LSTM Models
TI  - 10.31586/ujcsc.2025.1249
ER  -