發(fā)布時(shí)間：2018-06-17 03:04

  本文選題：社交媒體 + 旅游數(shù)據(jù)��； 參考：《西南科技大學(xué)》2016年碩士論文

【摘要】：近年來(lái),隨著在線(xiàn)社交媒體的發(fā)展和普及,越來(lái)越多的游客傾向于隨時(shí)隨地在社交媒體上發(fā)布旅游信息,產(chǎn)生了海量的、多維度、非結(jié)構(gòu)化的旅游數(shù)據(jù)。面向這種復(fù)雜數(shù)據(jù)的研究吸引了廣大高校和企業(yè)界的廣泛關(guān)注。本文從三個(gè)方面介紹了社交媒體上旅游數(shù)據(jù)的研究工作:首先是旅游數(shù)據(jù)的采集以及預(yù)處理,其次是基于旅游數(shù)據(jù)的分析,包括Top-k支配查詢(xún)算法、文本情感挖掘技術(shù)、關(guān)鍵詞提取技術(shù)等,最后是基于旅游數(shù)據(jù)的可視化研究。1、針對(duì)社交媒體上旅游數(shù)據(jù)的采集以及預(yù)處理,首先介紹了獲取旅游社交網(wǎng)站旅游數(shù)據(jù)的過(guò)程,其次對(duì)比分析抓包方式和模擬瀏覽器方式獲取微博數(shù)據(jù),接著介紹了如何通過(guò)搜索功能獲取微博數(shù)據(jù),最后從數(shù)據(jù)清洗和數(shù)據(jù)集成的角度對(duì)數(shù)據(jù)預(yù)處理。2、基于旅游數(shù)據(jù)的分析,為滿(mǎn)足子空間Top-k支配查詢(xún)需求,本文展開(kāi)了Topk支配查詢(xún)算法的研究。首先采用B+-Tree構(gòu)建有序列表,接著采用輪詢(xún)調(diào)度算法根據(jù)查詢(xún)條件獲取k組終結(jié)元組,其次,根據(jù)生成的候選元組和終結(jié)元組,采用概率分布模型計(jì)算終結(jié)元組支配分?jǐn)?shù)。迭代上述過(guò)程優(yōu)化查詢(xún)結(jié)果,直到滿(mǎn)足條件為止。本文采用SVM對(duì)短文本情感分類(lèi),特征選取包括標(biāo)點(diǎn)符號(hào)、標(biāo)簽、情感詞等。從實(shí)驗(yàn)結(jié)果來(lái)看,本文的方法具有一定的使用價(jià)值。3、基于旅游數(shù)據(jù)的網(wǎng)絡(luò)輿情,提出了一種面向?qū)ο蟮目梢暦治鯳eb框架,可以有效地提高了團(tuán)隊(duì)協(xié)同開(kāi)發(fā)的速度。本文設(shè)計(jì)并開(kāi)發(fā)了針對(duì)旅游網(wǎng)絡(luò)輿情的可視化分析系統(tǒng),該系統(tǒng)支持游客地點(diǎn)信息、評(píng)論情感信息、社交網(wǎng)絡(luò)信息可視化顯示和交互分析,從而方便用戶(hù)多角度地理解游客的輿情信息,發(fā)現(xiàn)評(píng)論中隱含的特征、關(guān)系和趨勢(shì)等。大量實(shí)驗(yàn)結(jié)果表明了該系統(tǒng)不僅能有效的分析游客地域傾向和情感變化,而且還幫助旅游管理部門(mén)及時(shí)了解旅游網(wǎng)絡(luò)輿情。
[Abstract]:In recent years, with the development and popularization of online social media, more and more tourists tend to publish travel information on social media anytime and anywhere, which produces massive, multi-dimensional, unstructured travel data. The research of this kind of complex data attracts the extensive attention of universities and business circles. This paper introduces the research work of tourism data on social media from three aspects: first, the collection and preprocessing of tourism data; secondly, the analysis based on tourism data, including Top-k dominating query algorithm, text emotion mining technology. Finally, based on the visualization research of tourism data, aiming at the collection and preprocessing of tourism data on social media, this paper first introduces the process of obtaining tourism data of tourism social network. Secondly, the paper compares and analyzes how to obtain Weibo data by means of packet capture and simulation browser, and then introduces how to obtain Weibo data by searching function. Finally, it analyzes the data preprocessing from the angle of data cleaning and data integration, based on the analysis of travel data. In order to satisfy the demand of subspace Top-k dominating query, this paper develops the research of Topk dominating query algorithm. First, B Tree is used to construct an ordered list, then polling scheduling algorithm is used to obtain k terminal tuples according to the query conditions. Secondly, a probability distribution model is used to calculate the final tuple dominating fraction according to the candidate tuple and the final tuple. Iterate the above procedure to optimize the query results until the conditions are met. In this paper, SVM is used to classify the emotion of short text. The feature selection includes punctuation, label, affective words and so on. According to the experimental results, the method of this paper has some practical value. Based on the network public opinion of tourism data, an object-oriented visual analysis Web framework is proposed, which can effectively improve the speed of team collaborative development. This paper designs and develops a visual analysis system for tourism network public opinion. The system supports tourist location information, comments emotional information, social network information visual display and interactive analysis. It is convenient for users to understand tourists' public opinion information from many angles, and to discover the implied features, relationships and trends in the comments. A large number of experimental results show that the system can not only effectively analyze the regional tendency and emotional change of tourists, but also help the tourism management department to understand the tourism network public opinion in a timely manner.
【學(xué)位授予單位】：西南科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：TP274
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本文編號(hào)：2029360