Design of news sentiment classification and recommendation system based on multi-model fusion and text similarity

Abstract

• The study proposes a sentiment classification method that combines multiple models and a recommendation algorithm based on text similarity calculation to enhance the performance of the news recommendation system. • The sentiment classification method achieves a precision rate of 0.95, outperforming the support vector machine by 0.10. • The recommendation algorithm based on text similarity calculation outperforms collaborative filtering and matrix decomposition algorithms, with accuracy, recall, and F1 values of 0.806, 0.804, and 0.806, respectively. • The personalized news recommendation system demonstrates a response time of less than 500ms for personalized recommendations and less than 300ms for system recommendations. • The system operates with low software resource consumption (less than 40 %) and hardware resource consumption (less than 45 %), meeting user needs with an average satisfaction rating higher than 90 points. To improve the performance of the news recommendation system, the study employs a multi-model fusion strategy in conjunction with three classification methods to improve the accuracy of sentiment classification. In addition, a text similarity-based recommendation algorithm is proposed to provide personalized recommendations to users. This is achieved by analyzing their interest features and key text features. In addition, a comprehensive recommendation system including news sentiment classification, recommendation algorithms, and a user interface is developed. The study showed that the sentiment classification method based on multi-model fusion had an accuracy rate of 0.95, which was an improvement of 0.10 compared to the support vector machine. Furthermore, the recommendation algorithm based on text similarity calculation demonstrated an accuracy, recall, and F1 value of 0.806, 0.804, and 0.806, respectively, which outperformed the collaborative filtering algorithm and the matrix decomposition recommendation algorithm. The average satisfaction rating for the news recommendation content was above 90 points, indicating that it met user needs. The findings indicate that consumers perceive the personalized news suggestion system to be well-received and that it functions effectively in terms of reaction time and resource consumption. This research offers an effective solution for news recommendation in the era of information overload, which holds significant theoretical and practical value.

Topics & Concepts