本文選題：話題檢測 + 演化分析　； 參考：《哈爾濱工業(yè)大學(xué)》2017年碩士論文

【摘要】：新浪微博目前是國內(nèi)最大的微博服務(wù)平臺,微博流中包含著數(shù)量眾多的,各個領(lǐng)域的新聞事件。目前新浪上有各種各樣的帶標(biāo)簽的話題事件,已有很多的研究針對于微博上的話題檢測,但很少有專門針對特定話題進(jìn)行研究的。一些熱點(diǎn)事件,起源于微博,在社會上引起了巨大的轟動,作為新聞媒體或者事件相關(guān)的公關(guān)團(tuán)隊(duì),更是極為關(guān)注該事件的走向,因此針對目標(biāo)話題的檢測與演化分析具有很好的市場需求。話題分為多種類別,包括突發(fā)事件,熱點(diǎn)事件等等,通過對現(xiàn)有的微博話題檢測方法進(jìn)行分析,發(fā)現(xiàn)目前基于微博上的話題檢測很少是有目標(biāo)性的檢測,并且要么檢測類別單一,要么無法區(qū)分所檢測事件類別。本文在總結(jié)了前人工作的基礎(chǔ)上,主要針對上述問題,從以下幾個方面進(jìn)行研究:第一,我們介紹了微博內(nèi)容的特點(diǎn),結(jié)合微博的話題標(biāo)簽及關(guān)聯(lián)規(guī)則挖掘方法,研究了如何針對特定目標(biāo)話題進(jìn)行話題檢測與跟蹤演化分析。第二,考慮到話題可以分為多種類別(突發(fā)事件、熱點(diǎn)事件、消逝的事件等),我們借鑒了關(guān)聯(lián)規(guī)則在商場顧客購物習(xí)慣上的應(yīng)用方法,分析了微博話題標(biāo)簽在微博中的作用,從微博的話題標(biāo)簽入手,修改并提出了關(guān)于關(guān)聯(lián)規(guī)則的4種演化模式,即新規(guī)則、熱點(diǎn)事件規(guī)則、變化中的規(guī)則、消逝的規(guī)則等,從而達(dá)到同時檢測多種類別話題,并明確其各自所屬類別的目的,以便為后續(xù)的話題演化分析提供支持。第三,針對目標(biāo)話題的演化進(jìn)行跟蹤。將我們話題檢測中所用到方法直接運(yùn)用于演化分析中,利用關(guān)聯(lián)規(guī)則的4種演化模式對目標(biāo)話題進(jìn)行演化分析。采用一種方法同時完成話題的檢測與演化分析的任務(wù),一定程度上降低了話題檢測與演化分析鏈接的復(fù)雜度。第四,將現(xiàn)有的應(yīng)用到微博話題檢測上的方法應(yīng)用到我們的場景中,并與我們的方法進(jìn)行對比,對各種方法的結(jié)果分析進(jìn)行了探討。本文實(shí)驗(yàn)結(jié)果證明了以下幾點(diǎn):第一,我們的方法可以有效的對特定目標(biāo)話題進(jìn)行檢測與演化分析。第二,論文中所采用的方法可以同時檢測不同類別的話題,并明確其所屬類別。第三,本論文中的方法可以同時用于微博的話題檢測與演化分析。
[Abstract]:Sina Weibo is currently the largest service platform in the country, Weibo flow contains a large number of news events in various fields. At present, there are a variety of tagged topic events on Sina. There have been many studies on topic detection on Weibo, but few have focused specifically on specific topics. Some hot events, originated from Weibo, have caused a great stir in society. As news media or public relations teams related to the incident, they are particularly concerned about the trend of the incident. Therefore, the detection and evolution analysis of target topics has a good market demand. Topics are divided into many categories, including emergencies, hot events and so on. By analyzing the existing methods of topic detection of Weibo, we find that topic detection based on Weibo is rarely targeted detection. And either detect a single category, or can not distinguish the category of events detected. On the basis of summarizing the previous work, this paper mainly studies the above problems from the following aspects: first, we introduce the characteristics of Weibo's content, combined with the topic label and association rules mining method of Weibo, This paper studies how to analyze the topic detection and tracking evolution for specific target topics. Second, considering that the topic can be divided into many categories (unexpected events, hot events, evanescent events, etc.), we draw lessons from the application of association rules in shopping habits of shopping malls, and analyze the role of Weibo topic labels in Weibo. Starting with Weibo's topic label, this paper modifies and puts forward four evolution modes of association rules, that is, new rules, hot event rules, changing rules, vanishing rules, and so on, so as to detect many kinds of topics simultaneously. The purpose of their respective categories is defined in order to provide support for the subsequent analysis of topic evolution. Third, track the evolution of the target topic. The methods used in topic detection are directly applied to evolutionary analysis, and four evolutionary models of association rules are used to analyze the evolution of target topics. The task of topic detection and evolution analysis is accomplished simultaneously by a method, which reduces the complexity of the link between topic detection and evolution analysis to a certain extent. Fourth, the existing methods applied to Weibo topic detection are applied to our scene, and compared with our methods, the results of various methods are discussed. The experimental results show that: first, our method can effectively detect and analyze the specific target topics. Secondly, the methods used in this paper can detect different categories of topics simultaneously and identify their categories. Thirdly, the method in this paper can be used for Weibo's topic detection and evolution analysis.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.1;TP393.092

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