期刊导航

论文摘要

基于BP神经网络的网络小说排行预测

Prediction of online novel rankings based on BP neural network

作者:龙彬(四川大学计算机学院,78179部队);胡思才(四川大学计算机学院,61920部队);李旭伟(四川大学计算机学院);郭峻铭(四川大学计算机学院)

Author:longbin(College of Computer Science, Sichuan University,Unit78179,PLA);husicai(College of Computer Science, Sichuan University,Unit 61920,PLA);lixuwei(College of Computer Science, Sichuan University);guojunming(College of Computer Science, Sichuan University)

收稿日期:2018-05-03          年卷(期)页码:2019,56(1):50-56

期刊名称:四川大学学报: 自然科学版

Journal Name:Journal of Sichuan University (Natural Science Edition)

关键字:“IP”概念 小说排行预测 BP神经网络 网络爬虫 ROC曲线 AUC值

Key words:"IP" concept;the ranking of the novel predicts;BP neural network; web crawler;ROC curve;AUC value

基金项目:国家自然科学基金(61173099)

中文摘要

近年来随着“IP”热潮兴起,网络文学市场发展迅速,逐渐成为文化娱乐行业投资热点.本文将机器学习方法引入到小说排行预测方面,通过网络爬虫获取网络小说信息,提取影响排行的特征,构建BP神经网络模型预测小说排行,针对预测结果非均衡的情况,采用ROC曲线和AUC值作为分类性能指标,得到较准确的预测精度,相比传统的文学定性分析方法,机器学习预测方法可解释性和可应用性更高.

英文摘要

In recent years, with the rise of the "IP" boom, the market of online literature is developing rapidly, has gradually become a popular type of entertainment investment industry. This paper introduces the machine learning method to the prediction of the novel rankings, obtains the network novel information through the web crawler, extracts the characteristics of the influence rankings, constructs the BP neural network model to predict the range of the novel rankings. In view of the non equilibrium of the prediction results, the ROC curves and AUC value used as the classification performance metrics, the accuracy is more accurate. Compared with the traditional literature qualitative analysis method, machine learning method is more predictable, interpretable and applicable.

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