Emotion Analysis of Literary Works Based on Qutrit-inspired Fully Self-supervised Quantum Neural Network

Abstract

The development of foreign literature, embodiment of emotional value in modern, contemporary foreign literature is more attentive on experience, through reading it, one can understand humanistic, personal feelings embedded in the work and grasp the author’s personality, spiritual experience. To analyze sentiment data of foreign literary works, proposed Emotion Analysis of Literary Works Based on Qutrit-inspired Fully Self-supervised Quantum Neural Network Method (EA-LW- QIFSQN). Initially input data are collected from NLP-dataset. Afterward, the input data provided to preprocessing. In preprocessing segment Federated Neural Collaborative Filtering (FNCF) is used to clean the unwanted data. Then preprocessed data is fed to feature extraction, synchro spline-kernelled chirplet extracting transform (SKCET) is used to extract two features such as textual features and lexical features. Afterwards QIFSQN is used to classify the emotions likes joy, sadness, anger, fear. Generally, QIFSQN doesn’t show some optimization adaption techniques to determine optimum parameter to offer accurate detection. Polar Coordinate Bald Eagle Search Algorithm (PCBSOA) is proposed to enhance QICCN classifies the emotions accurately. The proposed technique is executed and efficacy of EA-LW- QIFSQN technique is assessed with support of numerous performances like accuracy, recall, precision and F1-scorce is analyzed. Then, performance of EA-LW- QIFSQN technique is analyzed with existing techniques like emotion analysis of literary works depend on attention mechanisms with fusion of two-channel features (EA-LW-CNN), integrative improvement of modern literary works with traditional culture combined by semantic association network modeling(EA-LW-SAN) and emotion expression in modern literary appreciation: emotion-depend analysis (EA-LW-RFA) respectively.