本文選題：權(quán)重Wap算法 + 節(jié)點(diǎn)重要性　； 參考：《天津大學(xué)》2014年碩士論文

【摘要】：Web2.0網(wǎng)絡(luò)時代的到來，帶動了以新浪微博為代表的在線社交網(wǎng)絡(luò)平臺的迅速崛起。其用戶數(shù)量隨著市場規(guī)模的擴(kuò)大不斷激增。在線社交網(wǎng)絡(luò)平臺不僅僅是普通用戶的交流平臺。越來越多的企業(yè)在低成本和高利益的刺激下也紛紛加入。將對在線社交網(wǎng)絡(luò)的研究與更為廣泛的商業(yè)行為相結(jié)合，不僅能通過充分利用在線社交網(wǎng)絡(luò)的低成本、海量用戶、不受時間、空間、種族、文化等限制的優(yōu)點(diǎn)為企業(yè)帶來更高的經(jīng)濟(jì)效益，同時也能提高在線社交網(wǎng)絡(luò)平臺的用戶體驗(yàn)。本文以新浪微博在線社交網(wǎng)絡(luò)平臺作為分析對象，通過對中國移動公司的一條官方微博進(jìn)行分析，旨在獲得該微博信息在其傳播路徑上的重要用戶。 本文以Wap序列分析算法為基礎(chǔ)，提出并實(shí)現(xiàn)了一種改進(jìn)的權(quán)重Wap算法。該權(quán)重Wap算法引入了節(jié)點(diǎn)權(quán)重參數(shù)，允許企業(yè)根據(jù)其廣告營銷成本，設(shè)定合理的節(jié)點(diǎn)權(quán)重閾值，，進(jìn)而在構(gòu)建權(quán)重Wap-Tree樹型結(jié)構(gòu)時有效過濾掉不符合節(jié)點(diǎn)權(quán)重閾值的節(jié)點(diǎn)用戶。通過對節(jié)點(diǎn)權(quán)重參數(shù)的合理設(shè)置，能有效控制權(quán)重Wap算法挖掘出的頻繁序列數(shù)量以及重要用戶數(shù)量。與傳統(tǒng)的圖論角度的挖掘算法不同，本文沒有假設(shè)信息傳播路徑最優(yōu)，而是通過實(shí)驗(yàn)仿真的方式，生成了7萬條盡可能貼近現(xiàn)實(shí)的信息傳播路徑序列數(shù)據(jù)，并提出了一種以樹型結(jié)構(gòu)來存儲這些數(shù)據(jù)的思想，很好的展示了信息傳播的方向性。文章通過語義分析算法對信息傳播路徑中的每個用戶的評論進(jìn)行分析，并將積極評論與消極評論的比例定義為節(jié)點(diǎn)權(quán)重，能夠確保通過權(quán)重Wap算法挖掘出的重要用戶對產(chǎn)品的宣傳均是積極的、正面的。同時，文章提出了FileNet平臺下企業(yè)申請數(shù)據(jù)挖掘和廣告投放服務(wù)的自動化流程，實(shí)現(xiàn)了自動向重要用戶發(fā)放廣告的功能。經(jīng)實(shí)驗(yàn)證明，權(quán)重Wap算法在現(xiàn)實(shí)意義、準(zhǔn)確率與時間復(fù)雜度、信息傳播方向性、剔除消極影響等方面均具有較大的優(yōu)勢。
[Abstract]:The advent of the Web 2.0 era has led to the rapid rise of online social networking platforms represented by Sina Weibo. The number of users along with the expansion of the market scale continues to surge. The online social network platform is not just the communication platform for ordinary users. More and more enterprises in low-cost and high-interest incentives have joined. Combining research on online social networks with broader business practices, not only by taking full advantage of the low cost of online social networks, a large number of users, regardless of time, space, race, The advantages of culture and other restrictions can bring higher economic benefits for enterprises, but also improve the online social network platform user experience. Based on Sina Weibo online social network platform, this paper analyzes an official Weibo of China Mobile in order to obtain the important users of the Weibo information in its transmission path. Based on the Wap sequence analysis algorithm, an improved weighted Wap algorithm is proposed and implemented in this paper. The weighted Wap algorithm introduces the node weight parameter, which allows the enterprise to set a reasonable weight threshold according to its advertising cost, and then effectively filter out the node users who do not conform to the node weight threshold when constructing the weight Wap-Tree tree structure. By setting the weight parameters reasonably, the number of frequent sequences and the number of important users can be effectively controlled by the weighted Wap algorithm. Different from the traditional mining algorithm of graph theory, this paper does not assume that the information transmission path is optimal, but generates 70,000 information transmission path sequence data which are as close to the reality as possible through experimental simulation. A tree structure is proposed to store these data, which shows the direction of information transmission. In this paper, the semantic analysis algorithm is used to analyze the comments of each user in the information transmission path, and the ratio of positive comments to negative comments is defined as the node weight. It can ensure that the important users' propaganda of the product is positive and positive by the weighted Wap algorithm. At the same time, the paper puts forward the automatic process of enterprise application data mining and advertising service under FileNet platform, and realizes the function of distributing advertisements to important users automatically. The experiments show that the weighted Wap algorithm has great advantages in practical significance, accuracy and time complexity, direction of information propagation, elimination of negative effects, and so on.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP393.092

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 張紫瓊;葉強(qiáng);李一軍;;互聯(lián)網(wǎng)商品評論情感分析研究綜述[J];管理科學(xué)學(xué)報;2010年06期

2 王慧;張駿溫;;基于改進(jìn)的Wap算法的Web序列模式的研究[J];計算機(jī)科學(xué);2012年02期

3 康海燕,樊孝忠,湯世平;基于J2EE的在線測評系統(tǒng)的研究與設(shè)計[J];計算機(jī)工程;2004年13期

4 婁德成;姚天f ;;漢語句子語義極性分析和觀點(diǎn)抽取方法的研究[J];計算機(jī)應(yīng)用;2006年11期

5 朱嫣嵐;閔錦;周雅倩;黃萱菁;吳立德;;基于HowNet的詞匯語義傾向計算[J];中文信息學(xué)報;2006年01期

6 王振宇;吳澤衡;胡方濤;;基于HowNet和PMI的詞語情感極性計算[J];計算機(jī)工程;2012年15期

7 佟雅娟;;FileNet平臺下企業(yè)通用流程模塊的設(shè)計與實(shí)現(xiàn)[J];計算機(jī)與現(xiàn)代化;2013年05期

8 趙妍妍;秦兵;劉挺;;文本情感分析[J];軟件學(xué)報;2010年08期

9 張成功;劉培玉;朱振方;方明;;一種基于極性詞典的情感分析方法[J];山東大學(xué)學(xué)報(理學(xué)版);2012年03期

10 吳思竹;張智雄;;網(wǎng)絡(luò)中心度計算方法研究綜述[J];圖書情報工作;2010年18期

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