A Novel Deep Learning Model for Real-Time Sentiment Analysis in Social Media
Abstract
Sentiment analysis on social media platforms provides valuable insights into public opinion and trends. This paper introduces a novel deep learning model for real-time sentiment analysis, leveraging a hybrid architecture of convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM) networks. The model processes text data with high accuracy and speed, enabling real-time analysis of large-scale social media streams. Experiments on benchmark datasets and live Twitter feeds demonstrate the model's superior performance in sentiment classification tasks. The study highlights the potential of the proposed model for applications in marketing, politics, and crisis management.
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