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Sentiment Analysis Employing LSTM (Long Short-Term Memory) for Binary Classification of Social Media Texts
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 29 |
| Year of Publication: 2025 |
| Authors: Pournima Anil Kharade, Savita Sangam |
10.5120/ijca2025925500
|
Pournima Anil Kharade, Savita Sangam . Sentiment Analysis Employing LSTM (Long Short-Term Memory) for Binary Classification of Social Media Texts. International Journal of Computer Applications. 187, 29 ( Aug 2025), 41-48. DOI=10.5120/ijca2025925500
Abstract
Sentiment analysis is a domain within Natural Language Processing (NLP) focused on the computer detection and classification of opinions in textual data. The expansion of social media platforms, such as Twitter, Facebook, and Instagram, has resulted in an increase in user-generated content that reflects public opinion on various issues, including films, products, and political events, daily. An examination of this information would benefit firms, lawmakers, and other stakeholders by aiding in the evaluation of public perception. The traditional method of sentiment analysis relies on a rule-based framework and fundamental machine learning algorithms, including Naive Bayes, Support Vector Machines (SVM), and Logistic Regression (LR). These solutions generally necessitate manually generated features and face challenges in capturing more profound linkages and dependencies within the text. Recent advancements in deep learning, particularly in Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks, have transformed the domain of sentiment analysis by offering models for the unsupervised learning of associations from raw text data. As a variation of RNN, LSTM addresses the vanishing gradient problem, making it more suitable for tasks requiring longer dependencies, such as sentiment analysis. This study investigates the application of LSTM networks for binary sentiment categorisation utilising the IMDb movie review dataset. The model's learning capabilities were enhanced by using pre-trained word embeddings (GloVe), which illustrate semantic relationships among words and augment the model's contextual comprehension.
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