本文關(guān)鍵詞： 微博 數(shù)據(jù)獲取 話題檢測 時(shí)間相似度 用戶權(quán)威度　出處：《昆明理工大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著互聯(lián)網(wǎng)技術(shù)的迅猛發(fā)展,網(wǎng)絡(luò)媒體已經(jīng)成為人們?nèi)粘Ｉ钪胁豢苫蛉钡囊环N傳播媒介。其中,微博作為新興典型代表之一,以極快的速度影響著社會(huì)傳播格局。用戶可以通過微博隨時(shí)隨地的更新個(gè)人狀態(tài)和參與討論自己喜愛或關(guān)心的話題,使得微博成為社會(huì)熱點(diǎn)話題產(chǎn)生的聚集地。對微博進(jìn)行話題檢測,不僅能向用戶提供熱點(diǎn)話題信息,還能為政府部門進(jìn)行突發(fā)事件監(jiān)測、輿情分析等方面提供強(qiáng)有力的數(shù)據(jù)支持,因此研究如何從海量的微博信息中檢測出熱門話題具有十分重要的現(xiàn)實(shí)意義。 微博文本相對于傳統(tǒng)文本來說差異較大,除了大量的省略、指代及主觀性的個(gè)性化語言之外,還有文本較短,話題離散性、實(shí)時(shí)性和互動(dòng)性等特點(diǎn)。因而,傳統(tǒng)的文本話題檢測方法不能直接應(yīng)用于微博,故本文在研究過程中結(jié)合微博自身的特性,提出了一種基于用戶權(quán)威度的中文微博話題檢測方法。 首先,在本文的話題檢測算法中引入用戶威權(quán)度,把用戶的粉絲數(shù)量作為微博影響力的重要參考因素,與以往只針對無結(jié)構(gòu)文本分析的話題檢測算法相比,提高了話題檢測的準(zhǔn)確度；其次,一般的話題檢測方法不重視時(shí)間因素,而本文把時(shí)間因素作為檢測話題的重要參數(shù),把微博時(shí)間限制在一個(gè)有效期內(nèi),使得話題發(fā)現(xiàn)的更加精確；最后,傳統(tǒng)方法中話題檢測與話題排序是相互獨(dú)立的,不能直接利用檢測的結(jié)果對話題排序,而本文在對話題進(jìn)行檢測過程中,引入了基礎(chǔ)能量和相關(guān)性能量的概念并以此作為話題能量,在話題檢測完成后可以依據(jù)話題能量大小對話題直接進(jìn)行排序。
[Abstract]:With the rapid development of Internet technology, Internet media has become an indispensable media in people's daily life. At an extremely fast speed, it affects the social communication pattern. Users can update their personal status and participate in discussions on topics they like or care about through Weibo at any time and anywhere. Weibo has become a gathering place for hot social topics. The topic detection of Weibo can not only provide users with information on hot topics, but also monitor emergencies for government departments. The analysis of public opinion provides strong data support, so it is of great practical significance to study how to detect hot topics from mass Weibo information. Weibo's text is quite different from the traditional text. In addition to a large number of individualized languages such as ellipsis, reference and subjectivity, there are also features such as shorter text, discrete topic, real-time and interactive, etc. The traditional text topic detection method can not be directly applied to Weibo, so this paper puts forward a new topic detection method based on user authority, which is based on the characteristics of Weibo itself. First of all, the user authority is introduced into the topic detection algorithm in this paper, and the number of users' fans is regarded as an important reference factor of Weibo's influence, compared with the previous topic detection algorithm, which only focuses on unstructured text analysis. The accuracy of topic detection is improved. Secondly, the general method of topic detection does not attach importance to time factor, and this paper regards time factor as an important parameter of detecting topic, and limits Weibo's time to a period of validity. Finally, the traditional methods of topic detection and topic ranking are independent of each other, can not directly use the results of the detection of topics ranking, and this paper in the process of topic detection, The concepts of basic energy and correlation energy are introduced and used as topic energy. After topic detection is completed, topics can be sorted directly according to the magnitude of topic energy.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.092

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