本文選題：用戶生成內(nèi)容 + 主成分分析　； 參考：《北京郵電大學(xué)》2017年碩士論文

【摘要】：隨著互聯(lián)網(wǎng)技術(shù)的快速發(fā)展,由用戶主導(dǎo)生成內(nèi)容的Web2.0時代逐步發(fā)展成熟,近幾年移動互聯(lián)網(wǎng)技術(shù)的發(fā)展更是給人們的生活帶來巨大變革。用戶生成內(nèi)容(UGC)作為Web 2.0環(huán)境下一種新的內(nèi)容生成及組織形式,受到大家的廣泛關(guān)注。目前視頻分享網(wǎng)站、微博、博客、問答社區(qū)是比較主流的移動互聯(lián)網(wǎng)UGC業(yè)務(wù)形式。移動互聯(lián)網(wǎng)為UGC的發(fā)展注入了新的動力,近幾年用戶生成內(nèi)容的數(shù)量快速增加,然而也暴露出越來越多的質(zhì)量問題,其整體質(zhì)量還有待改善。選擇科學(xué)的評價方法對UGC質(zhì)量做出評價,才能使以UGC業(yè)務(wù)為主的網(wǎng)絡(luò)平臺的環(huán)境得到改善,在用戶生成內(nèi)容質(zhì)量評價的基礎(chǔ)上制定合理的激勵措施有利于讓用戶生成更多優(yōu)質(zhì)內(nèi)容。本文選取移動互聯(lián)網(wǎng)視頻UGC為研究對象,通過挖掘視頻相關(guān)的指標(biāo)數(shù)據(jù),使用分類算法對其質(zhì)量做出評價。在以往研究和視頻UGC本身特點(diǎn)的基礎(chǔ)上,構(gòu)建了包含對象層、維度層、測度層的質(zhì)量評價框架。對象層包括視頻制作水平、視頻內(nèi)容本身、視頻觀看體驗(yàn)、視頻內(nèi)容效用四個維度,確保對視頻內(nèi)容做出全面、準(zhǔn)確的評價。本研究在維度層指標(biāo)的基礎(chǔ)上還設(shè)計了維度層量化指標(biāo),根據(jù)維度層量化指標(biāo)對視頻進(jìn)行人工打分,使用主成分分析方法確定指標(biāo)權(quán)重,人工打分和指標(biāo)權(quán)重共同得到了基于主成分分析的視頻UGC質(zhì)量,然后對視頻質(zhì)量做出高、低質(zhì)量分類。本文還構(gòu)建了視頻UGC內(nèi)容、用戶交互關(guān)系模型,測度層指標(biāo)均取自該模型�；谥鞒煞址治龅囊曨l質(zhì)量分類結(jié)果和測度層指標(biāo)共同構(gòu)成了基于分類算法的視頻質(zhì)量評價模型,本文將應(yīng)用于該模型的樣本數(shù)據(jù)分為訓(xùn)練樣本集和測試樣本集,使用訓(xùn)練樣本集對模型進(jìn)行訓(xùn)練,然后使用訓(xùn)練后的模型對測試樣本集進(jìn)行質(zhì)量分類預(yù)測,結(jié)果表明該模型具有很強(qiáng)的可操作性和科學(xué)性。本文選取優(yōu)酷APP自頻道的用戶生成視頻內(nèi)容進(jìn)行實(shí)證分析,首先抓取了 892條視頻的測度層指標(biāo)數(shù)據(jù),然后通過問卷調(diào)查及主成分分析的方法得到這些視頻的質(zhì)量分類情況。測度層指標(biāo)數(shù)據(jù)和基于主成分分析的視頻質(zhì)量分類結(jié)果共同構(gòu)成了樣本數(shù)據(jù),對樣本數(shù)據(jù)進(jìn)行樣本平衡處理后,將樣本數(shù)據(jù)分為訓(xùn)練樣本集和測試樣本集。以測試樣本集為例,基于C5.0分類算法的質(zhì)量評價模型對視頻質(zhì)量的分類預(yù)測準(zhǔn)確率達(dá)到94.62%。最后本文還對四種分類算法的分類預(yù)測結(jié)果進(jìn)行了誤差對比及收益對比,結(jié)果表明C5.0算法的預(yù)測準(zhǔn)確率及預(yù)測收益均為最好。
[Abstract]:With the rapid development of Internet technology, the Web2.0 era of user-led content has gradually developed and matured. In recent years, the development of mobile Internet technology has brought great changes to people's lives. User generated content (UGC), as a new form of content generation and organization in Web 2.0 environment, has attracted wide attention. At present, video sharing website, Weibo, blog, Q & A community is the mainstream mobile Internet UGC service form. Mobile Internet has injected new impetus into the development of UGC. In recent years, the number of user-generated content has increased rapidly, but also exposed more and more quality problems, its overall quality needs to be improved. Only by choosing scientific evaluation methods to evaluate the quality of UGC, can the environment of network platform based on UGC service be improved. On the basis of quality evaluation of user-generated content, reasonable incentive measures can help users to generate more high-quality content. This paper selects the mobile Internet video UGC as the research object, through mining the video related index data, uses the classification algorithm to evaluate its quality. Based on the previous research and the characteristics of video UGC, a quality evaluation framework including object layer, dimension layer and measure layer is constructed. The object layer includes four dimensions: video production level, video content itself, video viewing experience, and video content utility, to ensure a comprehensive and accurate evaluation of video content. On the basis of dimensionality index, this study also designs dimension level quantization index, according to dimension level quantization index, the video is scored manually, and principal component analysis method is used to determine index weight. The quality of video UGC based on principal component analysis (PCA) is obtained by artificial scoring and index weight, and then the video quality is classified with high and low quality. This paper also constructs a video UGC content, user interaction model, the measurement layer indicators are taken from the model. The video quality classification results based on principal component analysis (PCA) and the measurement level indexes constitute the video quality evaluation model based on classification algorithm. In this paper, the sample data applied to the model are divided into training sample set and test sample set. The training sample set is used to train the model, and then the trained model is used to predict the quality of the test sample set. The results show that the model is feasible and scientific. In this paper, the user-generated video content of Youku APP self-channel is selected for empirical analysis. First, we grab the measure layer index data of 892 videos, and then obtain the quality classification of these videos by questionnaire and principal component analysis. The measure layer index data and the video quality classification results based on principal component analysis (PCA) constitute the sample data. After the sample data is balanced, the sample data is divided into the training sample set and the test sample set. Taking the test sample set as an example, the accuracy of video quality classification and prediction based on C5.0 classification algorithm is 94.622. Finally, the error and income of the four classification algorithms are compared, and the results show that the C5.0 algorithm has the best prediction accuracy and revenue.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F49

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