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Analysis of evaluation tendency of Python film based on adaptive SVM
Abstract
With the development of the economy and the improvement of people’s living standards, film, as an important part of enriching people’s cultural life, can help people enrich their leisure life through film, and at the same time, film can also achieve cultural exchanges between different countries. In the nearly 100 years of film development, people’s understanding of film has gradually deepened, making film consumption an indispensable part of the economy, Film evaluation plays a crucial role in film consumption and development. Many audiences choose to watch films not only based on the theme and cast, but also the evaluation of film and television works has become an important factor in the choice of watching films. At the same time, the level of film evaluation can indirectly reflect the audience’s recognition of the film, and also provide certain data support for the film producers and distributors, The film evaluation tendency will affect the box office of the film to a certain extent. As the related technologies in the field of prediction technology and statistical analysis become more mature, the content of film box office prediction and film review analysis has become an important functional module of many film platforms. Film evaluation and prediction will be affected by irregular data and lack of prediction accuracy. Using existing computing technology to achieve more accurate film evaluation and analysis can help better predict the box office performance of films.
Combining the current situation of the film market and the significance of film evaluation and prediction, this paper uses Python programming language to develop and design a system platform for film evaluation tendency analysis based on C/S structure. At the same time, in order to ensure the accuracy of film evaluation and analysis data information judgment, through adaptive SVM support vector machine algorithm application, the classification and extraction of data information is realized, Combining the content of film evaluation to achieve the classification of positive and negative film review digital information, combining the statistical legend of film evaluation to help better understand the tendency of film evaluation, combining data preprocessing, feature quantification and model structure confirmation to achieve the analysis of film review, which provides more detailed data information support for film box office prediction. Keywords: Python language; MySQL database; Adaptive SVM; Tendency of film evaluation;
目 录
摘 要 2
Abstract 3
第一章 绪论 1
1.1 开发背景 1
1.2研究现状 1
1.3研究的意义 2
1.4开发工具及技术 2
1.4.1 C/S模式简介 2
1.4.2 Mysql数据库 2
1.4.3 Python 语言 2
第二章 SVM的算法研究 3
2.1 SVM支持向量机介绍 3
2.2 自适应控制 3
第三章 电影评价倾向性需求分析 4
3.1需求调研 4
3.2可行性分析 4
3.2.1技术的可行性 4
3.2.2经济的可行性 4
3.2.3操作可行性 4
第四章 系统设计 5
4.1功能模块设计 5
4.2数据库设计 5
第五章 系统实现 6
5.1 系统主页 6
5.2评论管理界面 6
5.3用户管理 7
5.5评论预测界面 7
5.5 算法准确率比较 8
5.6 算法优缺点比较 8
5.7 自适应SVM与SVM对比 9
5.8 电影评价倾向性图表分析 9
第六章 系统测试 10
6.1 系统测试的意义 10
6.2 系统测试的内容 10
6.3系统测试结果 10
7. 总结与展望 11
7.1 总结 11
7.2 展望 11
致 谢 12
参考文献 13