本文選題：微博挖掘 + 情感分析��； 參考：《北京郵電大學(xué)》2016年碩士論文

【摘要】：隨著互聯(lián)網(wǎng)時(shí)代的到來(lái),社交媒體正日漸成為人們生活不可或缺的一部分。社交媒體為廣大用戶提供了一個(gè)即時(shí)的分享平臺(tái),用戶可以通過(guò)文本、圖片、音視頻等方式在平臺(tái)上分享消息,而平臺(tái)的社交屬性又使得這些消息可以沿著關(guān)注鏈呈指數(shù)級(jí)的傳播。除了作為網(wǎng)民提供信息的重要平臺(tái),社交媒體也已經(jīng)逐步成為網(wǎng)民獲取信息的重要渠道。而伴隨著移動(dòng)互聯(lián)網(wǎng)的興起,微博憑借其面向移動(dòng)用戶的特點(diǎn),得到了更加迅猛的發(fā)展。在全球范圍內(nèi),80%的網(wǎng)民都在使用社交網(wǎng)絡(luò)。中國(guó)互聯(lián)網(wǎng)絡(luò)信息中心報(bào)告顯示,截止到2015年6月,中國(guó)網(wǎng)民達(dá)到了 6.68億,其中,社交網(wǎng)絡(luò)的使用率超過(guò)7成,而微博使用率則超過(guò)30%。2015年第三季度,微博的月活躍用戶數(shù)已經(jīng)超過(guò)2億人,成為了中國(guó)最重要的社交媒體。微博的蓬勃發(fā)展帶來(lái)了大量的內(nèi)容信息,對(duì)于這些由用戶生成內(nèi)容進(jìn)行挖掘,具有十分重要的意義。微博內(nèi)容具備巨大的挖掘價(jià)值,這些信息一方面體現(xiàn)了用戶對(duì)于自身生活狀態(tài)、生活環(huán)境的態(tài)度,另一方面也包含了用戶對(duì)于關(guān)乎國(guó)計(jì)民生的大事的意見(jiàn)和聲音。通過(guò)對(duì)于微博的挖掘,提取出公眾對(duì)于金融領(lǐng)域和事件的態(tài)度和情緒,厘清公眾微博挖掘內(nèi)容和金融問(wèn)題的關(guān)系,對(duì)于個(gè)人和機(jī)構(gòu)的投資決策,以及管理機(jī)構(gòu)的政策制定都有重要的意義。對(duì)于微博平臺(tái)的挖掘,一方面可以針對(duì)用戶產(chǎn)生的內(nèi)容進(jìn)行挖掘,提取出用戶在發(fā)布內(nèi)容時(shí)傳遞出的潛在信息,另一方面,可以針對(duì)信息在傳播過(guò)程中體現(xiàn)出來(lái)的用戶關(guān)注關(guān)系網(wǎng)絡(luò)進(jìn)行挖掘。本論文基于上述思路,分別對(duì)微博進(jìn)行了情感分析和圖論分析,挖掘出微博文本和結(jié)構(gòu)中包含的信息。為了將微博挖掘結(jié)果應(yīng)用于金融領(lǐng)域,本論文設(shè)計(jì)了 一種基于金融主題模型的關(guān)聯(lián)算法,在微博與金融實(shí)體之間建立對(duì)應(yīng)關(guān)系。依據(jù)對(duì)應(yīng)關(guān)系,將微博挖掘結(jié)果與對(duì)應(yīng)的金融時(shí)間序列進(jìn)行關(guān)聯(lián)性分析。根據(jù)關(guān)聯(lián)性分析的結(jié)果,利用微博挖掘結(jié)果對(duì)金融時(shí)間序列進(jìn)行分析和預(yù)測(cè),并通過(guò)基于預(yù)測(cè)結(jié)果,實(shí)現(xiàn)自動(dòng)交易策略,驗(yàn)證了微博內(nèi)容挖掘結(jié)果對(duì)于金融時(shí)間序列預(yù)測(cè)的效果。
[Abstract]:With the advent of the Internet era, social media is increasingly becoming an indispensable part of people's lives. Social media provides a real-time platform for users to share messages on the platform through text, pictures, audio and video, etc. The social nature of the platform allows these messages to spread exponentially along the chain of concern. In addition to serving as an important platform for Internet users to provide information, social media has gradually become an important channel for Internet users to obtain information. With the rise of mobile Internet, Weibo has been developed more rapidly with its mobile-oriented characteristics. Around the world, 80% of Internet users are using social networks. According to the China Internet Network Information Center report, as of June 2015, the number of Chinese Internet users had reached 668 million, of which social network usage was more than 70 percent, while Weibo usage exceeded 30.2015 in the third quarter of 2015. Weibo has more than 200 million monthly active users, making it the most important social media in China. The vigorous development of Weibo brings a lot of content information, which is of great significance for the mining of user-generated content. The content of Weibo has great mining value. On the one hand, this information reflects the user's attitude towards their own living conditions and living environment, on the other hand, it also contains the users' opinions and voices on the major issues related to the national economy and the people's livelihood. Through the mining of Weibo, the attitudes and emotions of the public towards the financial field and events are extracted, the relationship between the contents of the public Weibo mining and financial problems is clarified, and the investment decisions of individuals and institutions are made. And the policy-making of the management organization has important significance. For the mining of Weibo platform, on the one hand, we can mine the content generated by the user, and extract the potential information that the user sends out when publishing the content, on the other hand, We can mine the user concern relation network which is reflected in the process of information dissemination. Based on the above ideas, this paper carries out affective analysis and graph theory analysis to Weibo, and excavates the information contained in Weibo text and structure. In order to apply the Weibo mining results to the financial field, this paper designs an association algorithm based on the financial subject model, and establishes the corresponding relationship between the Weibo and the financial entities. According to the corresponding relation, the correlation analysis between the Weibo mining results and the corresponding financial time series is carried out. According to the results of correlation analysis, the financial time series is analyzed and forecasted by using the results of Weibo mining, and the automatic trading strategy is realized based on the forecast results. The results of Weibo content mining are used to predict financial time series.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP391.1;TP393.092

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