Emotional computing modeling method combining user preference model

Emotional analysis refers to the analysis and identification of emotional tendencies and emotional expressions in texts throu gh natural language processing and computer technology. In order to improve the accuracy and efficiency of user emotion recognition, t his article proposes an algorithm based on deep learning (DL) and special optimization strategy. In order to verify the effectiveness and superiority of the algorithm, the performance of SVM algorithm and this a lgorithm in terms of accuracy, recall and recognition time under different node numbers and different data scales is compared through experiments. The experimental results show that the algorithm has obvious advantages in user emotion recognition and the s tability of search efficiency. Through the use of DL and special optimization strategies, this algorithm can better capture the emotional inform ation in the text and improve the accuracy of emotional recognition. Moreover, with the increase of data scale, multi node computing can effectively improve the efficiency of emotion recognition. The research results further confirm the advantages and application value of DL in the field of natural language processing, especially in the task of sentiment analysis.