Audience Feedback of Chinese Animated Movies Based on Sentiment Analysis Algorithm

• Published in: SSRG International Journal of Electrical and Electronics Engineering
• Main Author: Huang J.; Subri S.B.; Khalis F.
• Format: Article
• Language: English
• Published: Seventh Sense Research Group 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205600077&doi=10.14445%2f23488379%2fIJEEE-V11I9P106&partnerID=40&md5=7f67abe12c6151d596477946763594f2

This paper examines the audience response to Chinese animated movies through analysis of sentiment analysis to identify the perceptions and feelings of viewers. Recently, Chinese animation has achieved success due to its diverse narratives, which implies the need for audience study. Sources of data included social media and movie review websites, with data being preprocessed as follows: text normalization and feature extraction. Three sentiment analysis algorithms, namely VADER, SVM, and RNNs, were used to test the accuracy of sentiments in two categories of UGC. Table 1 indicates that RNNs had the highest accuracy of 85 percent, thus validating the ability of such models to capture compound opinions within texts in the Chinese language. Positive response-wise, positive sentiments dominated the sentiment analysis at 63%, and this demonstrates appreciation from the audience for works that embody cultural values from China. The findings of the study have several practical implications regarding the fine-tuning of content management processes to better engage the target audience and market the content contained to them. In the course of the analysis of the findings, attention was given to the ethical issues of the data use and proper handling of materials posted by the users. Further improvements to the current study can be made by conducting longitudinal research and comparing the results of the Chinese animation audience with those of other demographics to provide more comprehensive insights into the audience preferences and the industry in the Chinese context. © 2024 Seventh Sense Research Group.