Socio Veritas

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Garlapati Tripura Aditya; Karri Yathesh Vamsi Naidu; Alugolu Gowtham Kumar; Gorle Satya Venkata Naga Sai Kiran; Galla Ravi Teja

Socio Veritas

Authors: Garlapati Tripura Aditya, Karri Yathesh Vamsi Naidu, Alugolu Gowtham Kumar, Gorle Satya Venkata Naga Sai Kiran, Galla Ravi Teja

Unique Paper ID: 173650
Volume: 11
Issue: 10
PageNo: 1345-1351

Keywords: Machine Learning Algorithms Fake News Detection Long Short-Term Memory (LSTM) Text Classification Sentiment Analysis Dynamic Datasets.

Abstract:
The growing demand for accurate and efficient data classification has led to the widespread use of various machine learning algorithms. Existing systems primarily rely on traditional models such as Naïve Bayes, Decision Trees, Support Vector Machines (SVM), Neural Networks, Random Forest, and XGBoost for prediction and classification tasks. While these models deliver satisfactory results for static datasets, they face limitations when handling sequential or time-dependent data. This is especially true in tasks like fake news detection, where understanding context over time is crucial. The proposed system addresses these limitations by employing Long Short-Term Memory (LSTM), which are specifically designed to capture sequential dependencies and long-term contextual information. Unlike traditional models, LSTMs can retain memory of previous inputs, making them better suited for tasks involving text classification, sentiment analysis, and time-series prediction. By leveraging the memory capability of LSTMs, the proposed system aims to significantly improve classification accuracy, adaptability, and scalability, thus offering a more effective solution for fake news detection in complex and dynamic datasets.

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ISSN: 2349-6002
Volume: 11
Issue: 10
PageNo: 1345-1351

Socio Veritas

Available:https://ijirt.org/article?manuscript=173650

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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