【摘要】：近年來,隨著互聯(lián)網(wǎng)的飛速發(fā)展,網(wǎng)絡(luò)已經(jīng)成為人們?nèi)粘Ｉ钪蝎@取信息的主要渠道。微博作為近年來快速發(fā)展起來的網(wǎng)絡(luò)新興媒體,已積累上億用戶。微博平臺包含信息量大,信息更新速度快,常常使用戶淹沒在信息的海洋,幫助用戶找到影響力大的用戶所發(fā)表的微博信息具有重要意義。微博平臺推出的檢索功能是幫助用戶找尋微博信息的良好途徑。傳統(tǒng)的信息檢索包含相關(guān)性,權(quán)威性,時效性三個關(guān)鍵因素。微博平臺由于內(nèi)容更新快速,發(fā)表內(nèi)容用語不規(guī)范,所以時效性和權(quán)威性往往具有更加重要的意義。本文的影響力分析也是對權(quán)威性的研究。 本文利用微博數(shù)據(jù),對用戶的影響力進行分析研究,主要成果包括以下內(nèi)容： 1.微博數(shù)據(jù)的獲取。本文研究初期,從微博平臺抓取大量用戶數(shù)據(jù),包括用戶的詳細(xì)信息,用戶關(guān)注關(guān)系,回復(fù)轉(zhuǎn)發(fā)關(guān)系等。這部分?jǐn)?shù)據(jù)是本文研究的基礎(chǔ)工作,也可作為微博其他研究的基礎(chǔ)數(shù)據(jù)。 2.本文對于微博用戶影響力的研究,目標(biāo)是識別用戶在不同領(lǐng)域的不同影響力。本文從用戶發(fā)表的微博內(nèi)容及用戶之間的關(guān)注關(guān)系對微博用戶所屬領(lǐng)域進行劃分,并得出用戶在各個領(lǐng)域的權(quán)重。通過半自動的標(biāo)注樣本驗證,該劃分方法具有比較準(zhǔn)確的效果。 3.本文在對用戶發(fā)表的微博內(nèi)容做文本分析的同時,通過并行的新詞識別算法識別微博內(nèi)容中的新詞,并利用搜索引擎的相關(guān)搜索對重要文本特征做語義擴展,解決了微博文本內(nèi)容短小,特征稀疏,無意義特征過多,有區(qū)分度的特征較少等一系列問題。 4.本文利用用戶在不同領(lǐng)域的分類權(quán)重,基于用戶間的回復(fù)和轉(zhuǎn)發(fā)微博關(guān)系,構(gòu)建領(lǐng)域相關(guān)的影響力傳播模型,經(jīng)過對比驗證,該方法具有不錯的效果。
[Abstract]:In recent years, with the rapid development of the Internet, the Internet has become the main channel for people to obtain information in their daily life. Weibo as a rapid development in recent years network emerging media, has accumulated hundreds of millions of users. Weibo platform contains a large amount of information, information update speed, often make users submerged in the ocean of information, help users to find the influential user published by Weibo information is of great significance. Weibo platform launched the search function is to help users to find a good way to 348 _person1# information. Traditional information retrieval includes three key factors: relevance, authority and timeliness. Weibo's platform is of great significance because of its fast updating and non-standard content expression, so timeliness and authoritativeness are often more important. The influence analysis of this paper is also an authoritative study. This article uses Weibo data, carries on the analysis to the user's influence, the main achievement includes the following contents: 1. Weibo data acquisition. At the beginning of this paper, a large amount of user data was captured from Weibo platform, including user's detailed information, user concern relationship, reply forwarding relationship and so on. This part of data is the basic work of this study, but also can be used as Weibo other basic data. 2. The aim of this paper is to identify the influence of Weibo in different fields. According to Weibo's content published by users and the relationship of concern between users, this paper divides the user's domain into two parts, and gets the weight of user's every domain. The method is proved to be more accurate by semiautomatic labeling samples. 3. In this paper, we analyze the text of Weibo published by users, and recognize the new words in Weibo content by parallel neologism recognition algorithm, and extend the semantic features of important text by using the relevant search engine. It solves a series of problems, such as short content, sparse features, too many meaningless features, less distinguishing features and so on. 4. Based on users' classification weights in different domains and based on the relationship between users' reply and forwarding Weibo, a domain-related influence propagation model is constructed in this paper. The results show that this method has a good effect.
【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】：2013
【分類號】：TP393.092

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本文編號：2329497