發(fā)布時(shí)間：2018-02-12 23:32

  本文關(guān)鍵詞： 云南旅游 熱點(diǎn)話題 領(lǐng)域本體 特征提取 網(wǎng)絡(luò)輿情　出處：《云南財(cái)經(jīng)大學(xué)》2017年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：隨著人們生活質(zhì)量的不斷提高,旅游行業(yè)迅速崛起,在國(guó)民經(jīng)濟(jì)中占據(jù)著舉足輕重的地位。云南省順應(yīng)時(shí)代的發(fā)展,在當(dāng)前的發(fā)展優(yōu)勢(shì)下緊緊抓住這個(gè)機(jī)遇,大力發(fā)展旅游業(yè),現(xiàn)已成為國(guó)內(nèi)外知名的旅游勝地。信息技術(shù)隨著經(jīng)濟(jì)的發(fā)展而進(jìn)步,互聯(lián)網(wǎng)技術(shù)的快速發(fā)展和智能手機(jī)的普及導(dǎo)致使用社交軟件的門(mén)檻越來(lái)越低,各大社交網(wǎng)站已經(jīng)成為了各種有關(guān)云南旅游聲音匯聚的重要場(chǎng)所。眾多網(wǎng)民針對(duì)網(wǎng)絡(luò)旅游話題所持有的代表性意見(jiàn)隨著網(wǎng)絡(luò)輿論對(duì)旅游業(yè)影響力的不斷提升而擴(kuò)大,在這種局勢(shì)下,微博以其簡(jiǎn)單方便快捷的優(yōu)點(diǎn)迅速成為人們感興趣的平臺(tái),漸漸成為旅游網(wǎng)絡(luò)輿情的主要傳播途徑之一。為了長(zhǎng)期維護(hù)云南旅游業(yè)的安全穩(wěn)定和健康發(fā)展,發(fā)現(xiàn)微博上的云南旅游熱點(diǎn)話題并對(duì)旅游網(wǎng)絡(luò)輿情的演化趨勢(shì)進(jìn)行分析預(yù)測(cè)具有重大的現(xiàn)實(shí)意義。本文研究的主要內(nèi)容是:如何將網(wǎng)民們關(guān)注的旅游熱點(diǎn)話題從復(fù)雜的、分散的網(wǎng)絡(luò)數(shù)據(jù)中提取出來(lái);如何解析提取出來(lái)的旅游熱點(diǎn)話題的信息,并以此來(lái)分析該話題的演化趨勢(shì)。目前發(fā)現(xiàn)網(wǎng)絡(luò)熱點(diǎn)話題的大多數(shù)研究方法都是通過(guò)文本挖掘技術(shù)解釋信息內(nèi)容,并發(fā)現(xiàn)這些信息之間的關(guān)系,進(jìn)而挖掘出虛擬網(wǎng)絡(luò)中的網(wǎng)民們關(guān)注的熱點(diǎn)話題,但是效果都不是很理想,尤其在不同的領(lǐng)域,同樣的挖掘方法得到的結(jié)果卻不如人意。本文根據(jù)現(xiàn)在研究的不足,針對(duì)具體領(lǐng)域構(gòu)建領(lǐng)域本體,并將該領(lǐng)域本體應(yīng)用在輿情分析中的數(shù)據(jù)處理、文本建模和話題聚類(lèi)等過(guò)程中,從而發(fā)現(xiàn)人們關(guān)注度高的話題,最后按照人們對(duì)話題關(guān)注的熱度為話題排序,從而得到熱點(diǎn)話題。在此基礎(chǔ)上,綜合其他學(xué)科的相關(guān)理論來(lái)分析熱點(diǎn)話題的形成機(jī)制和演化特點(diǎn)來(lái)預(yù)測(cè)熱點(diǎn)話題的演化趨勢(shì)。本文在研究中所做的創(chuàng)新性工作如下:(1)基于云南旅游這個(gè)具體的領(lǐng)域構(gòu)建領(lǐng)域本體。(2)在深入研究特征提取算法的基礎(chǔ)上,提出一種基于領(lǐng)域本體的特征提取算法。該方法融合領(lǐng)域本體與TF-IDF方法,對(duì)本體推理從而優(yōu)化特征抽取,用改進(jìn)過(guò)的TF-IDF公式來(lái)計(jì)算特征詞的權(quán)重。利用傳統(tǒng)算法和改進(jìn)后的算法進(jìn)行實(shí)驗(yàn),實(shí)驗(yàn)結(jié)果對(duì)比表明了該優(yōu)化算法提高了特征抽取的正確率,證明了它是一種有效提取特征的方法。(3)依據(jù)微博評(píng)論數(shù)、點(diǎn)贊數(shù)和轉(zhuǎn)發(fā)數(shù)三者的調(diào)和數(shù)對(duì)旅游話題的熱度進(jìn)行評(píng)定,按照評(píng)估值對(duì)話題排序,將排序的結(jié)果與微博官方公布的排行進(jìn)行比對(duì),從而驗(yàn)證了該評(píng)估法的有效性。
[Abstract]:With the continuous improvement of people's quality of life and the rapid rise of the tourism industry, it occupies a pivotal position in the national economy. Yunnan Province conforms to the development of the times, grasps this opportunity tightly under the current development superiority, vigorously develops the tourism industry, It has become a well-known tourist destination at home and abroad. With the development of economy, the rapid development of Internet technology and the popularity of smartphones, the barriers to using social software are getting lower and lower. The major social networking sites have become important places for the gathering of various tourist voices in Yunnan. The representative opinions held by many netizens on the topic of online tourism have expanded with the increasing influence of Internet public opinion on tourism. In this situation, Weibo, with its simple, convenient and quick advantages, quickly became a platform of interest to people, and gradually became one of the main channels of dissemination of public opinion on the tourism network. In order to maintain the security, stability and healthy development of Yunnan's tourism industry for a long time, It is of great practical significance to find the hot topic of Yunnan tourism on Weibo and to analyze and predict the evolution trend of tourism network public opinion. Extract from scattered network data; how to parse extracted information on hot tourist topics, At present, most of the research methods to find the hot topics on the Internet are to interpret the information content through text mining technology, and to find the relationship between these information. The results of the same mining methods are not satisfactory, especially in different fields. According to the deficiency of the present research, this paper is based on the deficiency of the present research. The domain ontology is constructed according to the specific domain, and applied in the process of data processing, text modeling and topic clustering in the analysis of public opinion. Finally, the hot topics are sorted according to the heat that people pay attention to, and then the hot topics are obtained. On this basis, Based on the relevant theories of other disciplines, this paper analyzes the formation mechanism and evolution characteristics of hot topics to predict the evolution trend of hot topics. The innovative work done in this paper is as follows: 1) based on Yunnan tourism. Domain building domain ontology. 2) based on the in-depth study of feature extraction algorithm, A feature extraction algorithm based on domain ontology is proposed, which combines domain ontology with TF-IDF method and optimizes feature extraction by reasoning ontology. The weight of feature words is calculated by using the improved TF-IDF formula. The experimental results show that the proposed algorithm improves the accuracy of feature extraction by using the traditional algorithm and the improved algorithm. It is proved that it is an effective method to extract features.) according to the harmonic numbers of Weibo comment number, likes number and forwarding number, the heat of tourist topics is evaluated, and the topics are sorted according to the evaluation value. The ranking results were compared with Weibo's official ranking to verify the effectiveness of the evaluation method.
【學(xué)位授予單位】：云南財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP391.1

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 陳婷;王雪怡;曲霏;陳福集;;基于時(shí)序主題的網(wǎng)絡(luò)輿情熱點(diǎn)話題演化分析方法[J];華中師范大學(xué)學(xué)報(bào)(自然科學(xué)版);2016年05期

2 陳福集;馬梅蘭;;網(wǎng)絡(luò)輿情事件的話題演化分析——以成都女司機(jī)為例[J];情報(bào)雜志;2016年05期

3 李國(guó);張春杰;張志遠(yuǎn);;一種基于加權(quán)LDA模型的文本聚類(lèi)方法[J];中國(guó)民航大學(xué)學(xué)報(bào);2016年02期

4 李釗;李曉;王春梅;李誠(chéng);楊春;;一種基于MapReduce的文本聚類(lèi)方法研究[J];計(jì)算機(jī)科學(xué);2016年01期

5 董堅(jiān)峰;肖麗艷;;旅游突發(fā)事件中的網(wǎng)絡(luò)輿情預(yù)警研究[J];現(xiàn)代情報(bào);2015年06期

6 付業(yè)勤;鄭向敏;雷春;鄭文標(biāo);;旅游危機(jī)事件網(wǎng)絡(luò)輿情的本體分析[J];重慶理工大學(xué)學(xué)報(bào)(社會(huì)科學(xué));2015年02期

7 周泓;劉金嶺;王新功;;基于短文本信息流的回顧式話題識(shí)別模型[J];中文信息學(xué)報(bào);2015年01期

8 陳卓群;;基于共詞網(wǎng)絡(luò)的社交媒體話題演化分析[J];情報(bào)科學(xué);2015年01期

9 曾潤(rùn)喜;王晨曦;陳強(qiáng);;網(wǎng)絡(luò)輿情傳播階段與模型比較研究[J];情報(bào)雜志;2014年05期

10 趙旭劍;楊春明;李波;張暉;金培權(quán);岳麗華;戴文鍇;;一種基于特征演變的新聞話題演化挖掘方法[J];計(jì)算機(jī)學(xué)報(bào);2014年04期

相關(guān)博士學(xué)位論文 前2條

1 趙晨陽(yáng);個(gè)性化旅游信息服務(wù)系統(tǒng)的基礎(chǔ)理論與關(guān)鍵技術(shù)研究[D];蘭州大學(xué);2012年

2 高田;基于領(lǐng)域知識(shí)的旅游突發(fā)事件狀態(tài)評(píng)估與演化研究[D];北京郵電大學(xué);2011年

相關(guān)碩士學(xué)位論文 前6條

1 賀源;基于微博的熱點(diǎn)話題發(fā)現(xiàn)研究[D];石家莊鐵道大學(xué);2016年

2 張猛;基于領(lǐng)域本體的個(gè)性化旅游推薦系統(tǒng)的研究與實(shí)現(xiàn)[D];重慶大學(xué);2015年

3 高丹丹;學(xué)科知識(shí)本體的構(gòu)建及其應(yīng)用[D];電子科技大學(xué);2014年

4 李巖;基于微博的網(wǎng)絡(luò)熱點(diǎn)發(fā)現(xiàn)研究[D];江蘇科技大學(xué);2013年

5 單月光;基于微博的網(wǎng)絡(luò)輿情關(guān)鍵技術(shù)的研究與實(shí)現(xiàn)[D];電子科技大學(xué);2013年

6 程春惠;公安犯罪案件文本挖掘關(guān)鍵技術(shù)研究[D];浙江大學(xué);2010年

，

本文編號(hào)：1506817