A DEEP TRANSFER LEARNING FRAMEWORK FOR MULTI-PLATFORM SENTIMENT PREDICTION

Abstract

To address the challenge of sentiment classification in multi-source textual data, this study implements a sentiment analysis framework based on Natural Language Processing (NLP) techniques and a Long Short-Term Memory (LSTM) neural network. The system first performs text preprocessing, including data cleaning, tokenization, stop word removal, and word embedding generation, to prepare diverse datasets for analysis. The LSTM model is trained to capture long-range dependencies and contextual information in text, enabling accurate sentiment prediction. Experimental evaluation shows that the proposed model achieves high accuracy in recognizing sentiment categories, with an overall classification accuracy of 0.928 for five emotion classes and a minimum mean absolute error (MAE) of 0.128. The model demonstrates strong performance in terms of precision, recall, F1-score, and receiver operating characteristic (ROC) metrics, confirming its reliability for sentiment orientation recognition in heterogeneous data sources. This research highlights the effectiveness of combining NLP preprocessing with LSTM networks for robust multi-source sentiment analysis, contributing to the development of advanced text mining and opinion detection systems.