發(fā)布時(shí)間：2018-04-13 10:00

  本文選題：Twitter數(shù)據(jù) + 情感影響研究��； 參考：《北京郵電大學(xué)》2014年博士論文

【摘要】：Web2.0時(shí)代的技術(shù)發(fā)展正在不斷影響和改變著人們的生活。各種各樣的社會(huì)網(wǎng)絡(luò)服務(wù),給人們的在線互動(dòng)交流帶來(lái)了前所未有的便利與快捷,同時(shí)也開(kāi)啟了大規(guī)模真實(shí)數(shù)據(jù)的時(shí)代,為基于社會(huì)網(wǎng)絡(luò)的用戶行為研究提供了機(jī)會(huì)與挑戰(zhàn)。一方面,傳統(tǒng)研究中不易獲得的大量用戶數(shù)據(jù)及關(guān)系信息,現(xiàn)在可以由大型社會(huì)網(wǎng)絡(luò)服務(wù)平臺(tái)較為輕松地獲得；另一方面,處理這些大規(guī)模數(shù)據(jù)并從中抽取出有用的信息應(yīng)用于實(shí)際任務(wù),也為研究者們提出了新的難題。用戶的情感信息被認(rèn)為是能夠影響用戶行為決策的主要因素之一。隨著社會(huì)網(wǎng)絡(luò)服務(wù)的日益壯大和發(fā)展,如何利用從社會(huì)網(wǎng)絡(luò)數(shù)據(jù)中獲取到的相關(guān)情感信息來(lái)對(duì)網(wǎng)絡(luò)中的用戶行為和主觀觀點(diǎn)進(jìn)行分析和預(yù)測(cè),是非常具有實(shí)際研究意義的工作。 本文以真實(shí)社會(huì)網(wǎng)絡(luò)中出現(xiàn)的用戶和話題為對(duì)象,著重研究了在不同的用戶行為預(yù)測(cè)任務(wù)下情感影響模型的建立和分析,并通過(guò)實(shí)驗(yàn)給出了一系列有參考價(jià)值的結(jié)果。這三個(gè)研究任務(wù)分別為：用戶關(guān)系預(yù)測(cè),個(gè)性化話題推薦,以及用戶對(duì)話題的情感推測(cè)。各個(gè)任務(wù)中的實(shí)驗(yàn)結(jié)果均表明,有針對(duì)性地利用情感影響因素而建立的預(yù)測(cè)模型,能夠得到比已有方法更好的效果。 論文的主要工作和貢獻(xiàn)如下： (1)基于近年來(lái)最受歡迎的社會(huì)網(wǎng)絡(luò)服務(wù)之一的Twitter,爬取了一段時(shí)間內(nèi)的真實(shí)數(shù)據(jù),建立了一個(gè)可用于各項(xiàng)實(shí)驗(yàn)的Twitter數(shù)據(jù)庫(kù)。該數(shù)據(jù)庫(kù)不僅包括了Twitter用戶的基本信息,還可以提取到用戶間的關(guān)系網(wǎng)絡(luò),以及每個(gè)用戶在該時(shí)段內(nèi)的發(fā)布的消息文本。文中應(yīng)用了一個(gè)快速有效的情感分析工具對(duì)數(shù)據(jù)庫(kù)中的用戶消息文本進(jìn)行了情感類別標(biāo)記。 (2)對(duì)用戶關(guān)系預(yù)測(cè)中的情感影響進(jìn)行了研究。這部分工作中考慮的情感影響因素是用戶在社會(huì)網(wǎng)絡(luò)中帶有情感傾向的影響力。首先定義和計(jì)算了用戶情感影響力,并基于計(jì)算出的結(jié)果對(duì)用戶進(jìn)行了屬性劃分。本文將用戶情感影響力屬性作為新的特征,針對(duì)兩個(gè)不同的用戶關(guān)系預(yù)測(cè)子任務(wù)分別建立了情感影響模型SA-UFP和SA-RFP。對(duì)比實(shí)驗(yàn)的結(jié)果分析顯示,SA-UFP和SA-RFP模型能夠有效提高預(yù)測(cè)正確率。 (3)對(duì)個(gè)性化話題推薦中的情感影響進(jìn)行了研究。這部分工作中考慮的情感影響因素是社會(huì)網(wǎng)絡(luò)話題下用戶情感觀點(diǎn)分布的影響。文中提出了關(guān)于話題的情感分布特征,并在真實(shí)數(shù)據(jù)上對(duì)它們進(jìn)行了觀察分析,而后基于話題情感分布對(duì)用戶興趣的影響建立了SDA-TR話題推薦模型。通過(guò)與已有推薦模型進(jìn)行對(duì)比實(shí)驗(yàn)分析,證明了SDA-TR模型能夠更好地為用戶進(jìn)行個(gè)性化話題推薦。 (4)對(duì)用戶對(duì)話題的情感推測(cè)這一應(yīng)用任務(wù)中的情感影響進(jìn)行了研究。這部分工作中考慮的情感影響因素是朋友用戶間的相互情感影響。在分析了朋友用戶間的情感影響并驗(yàn)證了相關(guān)假設(shè)的基礎(chǔ)上,本文建立了SFMF推測(cè)模型。用戶對(duì)話題情感推測(cè)任務(wù)上的對(duì)比實(shí)驗(yàn)分析表明,考慮了情感影響的SFMF模型更為準(zhǔn)確有效。
[Abstract]:Web 2.0 technology development is continuously influencing and changing people ' s life . Various kinds of social networking services bring unprecedented convenience and shortcut to people ' s online interactive exchange , meanwhile , it also opens up the era of large - scale real data , and provides the opportunity and challenge for the research of user behavior based on social network . On the one hand , the large number of user data and relationship information which are not easily obtained in the traditional research can be easily obtained by the large social network service platform ;
On the other hand , processing these large - scale data and extracting useful information from them is a new challenge for researchers . The user ' s emotional information is thought to be one of the main factors that can influence the user ' s behavior decision - making . With the growth and development of social networking services , how to analyze and predict the user ' s behavior and subjective viewpoint from the social network data is very meaningful .

Based on the users and topics appearing in the real social network , this paper focuses on the establishment and analysis of the emotion influence model under different user behavior prediction tasks , and gives a series of valuable results through experiments . The three research tasks are : user relation prediction , personalized topic recommendation and user ' s emotion speculation about the topic .

The main work and contribution of the thesis are as follows :

( 1 ) Based on Twitter , one of the most popular social networking services in recent years , the real data over a period of time has been crawled , and a Twitter database that can be used in various experiments has been established . The database not only includes the basic information of Twitter users , but also the relationship network between users , as well as the text of messages published by each user during that period . A quick and effective emotion analysis tool is used to mark the user message text in the database .

( 2 ) The influence of emotion on user ' s relationship is studied . The affective factors considered in this part are the influence of user ' s emotional tendency in the social network . Firstly , we define and calculate the influence of user ' s emotion , and divide the attribute of the user based on the result of the calculation . This paper sets up the emotion influence model SA - UFP and SA - RFP respectively for two different user relationship prediction sub - tasks . The results show that SA - UFP and SA - RFP model can improve the prediction accuracy effectively .

( 3 ) The emotion influence in the recommendation of personalized topic is studied . The emotion influencing factor considered in this part is the influence of the user ' s emotional view distribution under the topic of social network . The article puts forward the emotion distribution characteristic of the topic , and then sets up the SDA - TR topic recommendation model based on the influence of the topic emotion distribution on the user ' s interest . Through the comparison experiment analysis with the existing recommendation model , it is proved that the SDA - TR model can make personalized topic recommendation better for the user .

( 4 ) The emotional influence of the user on the subject ' s emotion is studied . The affective factors considered in this part are mutual affection between friends and users . Based on the analysis of the emotional impact between friends and users and the related assumptions , a SFMF speculation model is established . The comparison between the user ' s emotion estimation task shows that the SFMF model considering the influence of emotion is more accurate and effective .

【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.09

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