Sentiment Analysis Algorithm Based on Deep Transfer Learning for Multi-Source Data Fusion

To tackle the challenge of data diversity in sentiment analysis and improve the accuracy and generalization ability of sentiment analysis, this study first cleans, denoises, and standardizes multi-source data, and then proposes an improved sentiment analysis framework based on deep transfer learning technology. The swarm intelligence optimization algorithm is introduced to optimize the deep transfer learning architecture. The experiment outcomes indicate that the artificial bee colony-improved particle swarm optimization algorithm designed in the study has the minimum convergence value under single-peak and multi-peak test functions, with a standard deviation of less than 0.25 and a success rate of 100% in optimization. The solution set obtained by the algorithm performs well in terms of hyper-volume and inverse generation distance, and is closest to the true Pareto frontier. The diversity and convergence of the algorithm’s solutions outperform other algorithms, ensuring the optimization requirements of the sentiment analysis architecture. The deep transfer learning sentiment analysis model designed based on this achieves the best classification accuracy, with a minimum mean absolute error of 0.128, and good performance in accuracy recall and receiver operation characteristics. The recognition of emotional diversity has high accuracy, with an accuracy of 0.928 for 5 categories. The cross entropy loss and Matthews correlation coefficient confirm the reliability of this method for sentiment orientation recognition in multi-source data. The research explores the use of deep transfer learning in multi-source data fusion sentiment analysis, enriches and improves sentiment analysis techniques, and promotes innovation and development of sentiment analysis technology.