Concept-Adaptive Deep Learning for Efficient Short Text Stream Classification

Anjana S; Albin S S

Concept-Adaptive Deep Learning for Efficient Short Text Stream Classification

Authors: Anjana S , Albin S S

Unique Paper ID: 162205
Volume: 10
Issue: 8
PageNo: 320-326

Keywords: Concept-Adaptive Deep Learning Short Text Stream Classification Convolutional Neural Networks (CNNs) Long Short-Term Memory (LSTM) Networks

Abstract:
This research introduces Concept-Adaptive Deep Learning, a novel approach addressing the challenges posed by short text streams in real-world applications, particularly within the dynamic context of social media. Focused on brevity, high volume, and rapid data influx, short text streams present difficulties for existing classification algorithms due to data sparsity and concept drift. Leveraging external resources and pretrained embedding models, Convolutional Neural Networks (CNNs) capture local patterns, while a flexible Long Short-Term Memory (LSTM) network adapts to varying characteristics of text streams, such as high volume and velocity. Distributed computing techniques enhance efficiency and scalability, with a unique concept drift factor facilitating dynamic adjustments in classification strategies. Rigorous experimental validation on real datasets showcases the proposed approach's effectiveness and efficiency, positioning it as a robust solution for accurate short text stream classification in dynamic, high-velocity data environments.

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ISSN: 2349-6002
Volume: 10
Issue: 8
PageNo: 320-326

Concept-Adaptive Deep Learning for Efficient Short Text Stream Classification

Available:https://ijirt.org/Article?manuscript=162205

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