Sentiment Analysis of Animated Film Reviews Using Intelligent Machine Learning.

Abstract

Film is an essential expression of a country's cultural soft power in terms of cross-cultural exchange. In addition, film is also the most direct and favourable means of communication. Along with the expansion and development of the Chinese film market, outstanding animation films have emerged in recent years. Animated films have both artistic and commercial properties and can not only have a cultural impact but can also contribute to economic growth. For this reason, our country is now paying more and more attention to the development of animated films. Specifically, animated films not only represent a country's cultural soft power and national image, but they are also a symbol of the strength of a country's cultural industry. As a reflection and extension of China's culture and ideology, animated films play an important role in enhancing cultural confidence and cultural export. In recent years, China's economy has shown a steady and sustained growth trend. At the same time, with the rapid development of internet technology, social networking has gradually penetrated into all aspects of people's lives. Various social networking forums, websites, and sites have emerged. While satisfying a wide range of needs, they also provide information on product reviews, social reviews, and service reviews. These reviews contain feedback from the reviewer about the subject of the review. Tapping into the emotions in these reviews can provide consumers with shopping references and help businesses to optimise their products and improve their business strategies. With the help of modern internet technology and information technology, the modern movie industry, such as Cat's Eye Movies and other internet entertainment service platforms, has developed a model of online ticketing, offline movie viewing, and online reviews and feedback. The content of the reviews on these movie websites fully reflects the attitudinal views of the movie-going community. These reviews play a decisive role in the box office and the further spread of culture. As a result, in order to better understand the audience's emotional tendencies and needs, it is necessary to carry out sentiment analysis and deep semantic mining of animated film reviews. As the evaluation of film works considers many factors and is complex and variable, the choice of model is crucial in the process of sentiment analysis. Machine learning models represented by deep neural networks are more tolerant of sentence noise and have strong information discrimination and feature self-learning capabilities. As a result, intelligent machine learning is more advantageous for sentiment classification tasks. This study is a combination of textual data mining and statistical analysis from the perspective of viewers' comments to study the online reviews of animation films from different countries. At the same time, this research hopes to uncover meaningful information from the film reviews and the gap between Chinese and other countries' animation films, in order to provide a little help for the rise of domestic animation films.