全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

国产电影票房影响因素分析及票房预测
Analysis of Influential Factors and Box Office Prediction of Domestic Films

DOI: 10.12677/AAM.2023.124184, PP. 1772-1784

Keywords: 电影票房,Lasso,随机森林,神经网络,影响因素
Movie Box Office
, Lasso, Random Forest, Neural Network, Influence Factor

Full-Text   Cite this paper   Add to My Lib

Abstract:

2021年我国发布的《“十四五”文化产业发展规划》中指出,要推进文化产业创新发展,而电影作为文化传播的重要载体,对文化产业的影响不容忽视。随着人们越来越重视精神层面的追求,电影作为大众的主要娱乐方式之一,其市场规模逐渐扩大,竞争也日趋激烈。如何使得票房利润最大化是一个非常值得研究的问题。本文将研究影响票房的因素并构建合理的票房预测模型。首先爬取电影网站2012~2021年票房为一千万以上的国产电影为研究样本,共805部。根据电影类型、演员影响力、导演影响力、上映档期、电影时长、电影评分、总场次、首周票房、平均票价、场均人次等多个变量,分别建立Lasso、随机森林、BP神经网络三种票房预测模型,并筛选出对总票房有显著影响的因素。通过评价指标进行比较得出基于BP神经网络得到的模型可以较好地预测电影票房。同时得到电影票房的影响因素错综复杂,其中上映档期、评分、电影类型都起到了重要作用。
In the “14th Five-Year Plan for the Development of Cultural Industries” released in 2021, it was pointed out that the innovative development of cultural industries should be promoted, and the in-fluence of movies, as an important carrier of cultural communication, on cultural industries cannot be ignored. As people increasingly value the pursuit of spirituality, movies, as one of the main forms of entertainment for the masses, are gradually expanding their market, and the competition is be-coming more intense. How to maximize box office profits is an issue worth studying. In this paper, we will study the factors that influence the box office and construct a reasonable box office predic-tion model. Firstly, we crawl the movie websites to find domestic movies with a box office of more than 10 million from 2012 to 2021, which are 805 movies in total. Three box office prediction mod-els, Lasso, Random Forest and BP Neural Network, were built based on several variables, such as movie genre, actor influence, director influence, release schedule, movie duration, movie rating, to-tal number of scenes, first week box office, average ticket price and average attendance, and the factors that have significant influence on total box office were screened out. By comparing the eval-uation metrics, the model obtained based on the BP Neural Network can better predict the box of-fice. It is also obtained that the influencing factors of movie box office are intricate, among which the release schedule, rating, and movie genre all play an important role.

References

[1]  国家统计局. 中国统计年鉴2019 [M]. 北京: 中国统计出版社, 2019.
[2]  Siering, M., Muntermann, J., Ra-jagopalan, B., et al. (2018) Explaining and Predicting Online Review Helpfulness: The Role of Content and Review-er-Related Signals. Decision Support Systems, 108, 1-12.
https://doi.org/10.1016/j.dss.2018.01.004
[3]  田源. 中国电影票房季节性分析和预测——基于季节趋势模型和季节ARIMA模型[J]. 现代商业, 2019, 538(21): 47-50.
[4]  杨朝强, 蒋卫丽, 邵党国. 基于LSTM模型的电影票房预测算法[J]. 数据通信, 2019, 192(5): 34-37.
[5]  宋玉萍, 朱家明, 杨琴. 基于随机森林回归模型的国产电影首周票房预测分析[J]. 高师理科学刊, 2021, 41(1): 1-26.
[6]  李旺泽, 郑列. 基于随机森林回归模型的国产电影票房预测[J]. 湖北工业大学学报, 2021, 35(1): 114-117.
[7]  李振兴. 机器学习在电影票房预测中的应用研究[D]: [硕士学位论文]. 西安: 西安石油大学, 2020.
[8]  孙昕. 基于Lasso方法的中国股市时滞性回归分析[D]: [硕士学位论文]. 大连: 大连理工大学, 2017.
[9]  崔凝凝, 唐嘉庚. 基于回归分析的中国电影票房影响因素研究[J]. 江苏商论, 2012, 334(8): 35-39.
[10]  张鑫, 郭振宇. 基于随机森林的影片票房预测[J]. 现代电影技术, 2016, 452(3): 13-17+37.
[11]  张溪源. 基于BP神经网络的电影票房预测研究[D]: [硕士学位论文]. 乌鲁木齐: 新疆财经大学, 2020.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133