Research on Emotional Perception of Game Characters Based on Semantic Network Analysis

The objective of this study is to analyze the influencing factors of game users’ emotional perception of game character design through a case study of online reviews of game characters, to provide a reference for improving character design. The pycharm development tool was used to crawl the text data about character design in online comments on the Bilibili platform (a cultural community and video website with a high concentration of young generation in China) as data samples, and after software deduplication and data cleaning, the text analysis method and ROST CM 6.0 software were used to formulate a custom chart for word segmentation, and the influencing factors of character design emotional perception were analyzed by word frequency analysis, semantic network construction, and sentiment analysis were carried out afterword segmentation. The results showed that the high-frequency words that game users paid attention to included the character's modeling elements, personality traits, Peking Opera culture, voice choice, etc. The semantic web of online reviews presents a “core-periphery” hierarchy. The content of user reviews is dominated by positive emotions, followed by neutral emotions, and the least negative emotions. The aesthetic beauty, temperament and charm, cultural expression and career shaping of game characters are the most significant influencing factors of game users’ emotional perception. Designers of game characters should consider aspects such as appearance design, character building, cultural integration, and professional embodiment, and design the characters that can most resonate with users and be most conducive to cultural inheritance.