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In China, the emotional response of diverse cultural bearers, including literature, movies, and other media, frequently mirrors the country's cultural improvement. Consequently, emotional analysis of the text and paintings may be used to examine the cultural evolution, determine its growth dynamics, and sort out its context. The project aims to investigate how well sophisticated affective computing performs in identifying and analyzing the emotional aspects of traditional Chinese literature and language. The study selects the Double Channel based Self-organized Residual Network-50 V2 (DC-SoResNet 50V2) algorithm based on a combination of deep learning algorithms and further improving the (DC-SoResNet 50V2) algorithm using Emperor penguin Colony optimizer (EPCO). Moreover, the color image and text features from traditional Chinese literature are extracted using hummingbird-based term frequency-inverse document frequency (HB-TF-IDF) and Stochastic Color Harmony Algorithm (SCHA). Comparisons are made between the proposed and standard approaches using the training and test sets, respectively. The training time of the proposed approach is stable at around 25.1 s, while the test time is steady at about 18.5 s. The emotion identification accuracy of the proposed method achieved average of 99.11 and 99.23% in testing and training sets for the entire datasets, respectively. Comprehensive analysis of classic literary works can be possible with the help of this research, which offers fresh viewpoints and theoretical references.
Lan Wang (Mon,) studied this question.