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

【摘要】：Web2.0時代的技術發(fā)展正在不斷影響和改變著人們的生活。各種各樣的社會網(wǎng)絡服務,給人們的在線互動交流帶來了前所未有的便利與快捷,同時也開啟了大規(guī)模真實數(shù)據(jù)的時代,為基于社會網(wǎng)絡的用戶行為研究提供了機會與挑戰(zhàn)。一方面,傳統(tǒng)研究中不易獲得的大量用戶數(shù)據(jù)及關系信息,現(xiàn)在可以由大型社會網(wǎng)絡服務平臺較為輕松地獲得；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用于實際任務,也為研究者們提出了新的難題。用戶的情感信息被認為是能夠影響用戶行為決策的主要因素之一。隨著社會網(wǎng)絡服務的日益壯大和發(fā)展,如何利用從社會網(wǎng)絡數(shù)據(jù)中獲取到的相關情感信息來對網(wǎng)絡中的用戶行為和主觀觀點進行分析和預測,是非常具有實際研究意義的工作。 本文以真實社會網(wǎng)絡中出現(xiàn)的用戶和話題為對象,著重研究了在不同的用戶行為預測任務下情感影響模型的建立和分析,并通過實驗給出了一系列有參考價值的結果。這三個研究任務分別為：用戶關系預測,個性化話題推薦,以及用戶對話題的情感推測。各個任務中的實驗結果均表明,有針對性地利用情感影響因素而建立的預測模型,能夠得到比已有方法更好的效果。 論文的主要工作和貢獻如下： (1)基于近年來最受歡迎的社會網(wǎng)絡服務之一的Twitter,爬取了一段時間內(nèi)的真實數(shù)據(jù),建立了一個可用于各項實驗的Twitter數(shù)據(jù)庫。該數(shù)據(jù)庫不僅包括了Twitter用戶的基本信息,還可以提取到用戶間的關系網(wǎng)絡,以及每個用戶在該時段內(nèi)的發(fā)布的消息文本。文中應用了一個快速有效的情感分析工具對數(shù)據(jù)庫中的用戶消息文本進行了情感類別標記。 (2)對用戶關系預測中的情感影響進行了研究。這部分工作中考慮的情感影響因素是用戶在社會網(wǎng)絡中帶有情感傾向的影響力。首先定義和計算了用戶情感影響力,并基于計算出的結果對用戶進行了屬性劃分。本文將用戶情感影響力屬性作為新的特征,針對兩個不同的用戶關系預測子任務分別建立了情感影響模型SA-UFP和SA-RFP。對比實驗的結果分析顯示,SA-UFP和SA-RFP模型能夠有效提高預測正確率。 (3)對個性化話題推薦中的情感影響進行了研究。這部分工作中考慮的情感影響因素是社會網(wǎng)絡話題下用戶情感觀點分布的影響。文中提出了關于話題的情感分布特征,并在真實數(shù)據(jù)上對它們進行了觀察分析,而后基于話題情感分布對用戶興趣的影響建立了SDA-TR話題推薦模型。通過與已有推薦模型進行對比實驗分析,證明了SDA-TR模型能夠更好地為用戶進行個性化話題推薦。 (4)對用戶對話題的情感推測這一應用任務中的情感影響進行了研究。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證了相關假設的基礎上,本文建立了SFMF推測模型。用戶對話題情感推測任務上的對比實驗分析表明,考慮了情感影響的SFMF模型更為準確有效。
[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 .

【學位授予單位】：北京郵電大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：TP393.09

【參考文獻】

相關期刊論文 前1條

1 姚奕;;Web2.0 CMS在交互式多媒體教學中的研究和應用[J];中國教育技術裝備;2008年17期

，

本文編號：1744008