本文關(guān)鍵詞：網(wǎng)絡(luò)輿情信息與現(xiàn)實(shí)交易行為的映射關(guān)系研究與應(yīng)用　出處：《上海大學(xué)》2016年博士論文　論文類型：學(xué)位論文

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【摘要】：隨著互聯(lián)網(wǎng)和社交媒體的快速發(fā)展,網(wǎng)絡(luò)輿情信息與現(xiàn)實(shí)交易行為越來(lái)越呈現(xiàn)高度融合的趨勢(shì)。互聯(lián)網(wǎng)從一個(gè)簡(jiǎn)單的信息發(fā)布技術(shù)平臺(tái)演變成為社會(huì)化媒體的主要載體,成為一個(gè)交互式的信息發(fā)布、共享、交流與協(xié)作的社會(huì)化網(wǎng)絡(luò),極大的改變了人們觀察社會(huì)和經(jīng)濟(jì)的方式。隨著參與性不斷提高,人們不再是被動(dòng)的接受知識(shí),而是主動(dòng)地發(fā)表各種觀點(diǎn)和評(píng)論,這些觀點(diǎn)和評(píng)論不僅可以實(shí)時(shí)表達(dá)人們的真實(shí)想法,而且還可以通過(guò)影響受眾者的心理進(jìn)而改變現(xiàn)實(shí)世界的活動(dòng)。目前,研究學(xué)者主要利用網(wǎng)絡(luò)輿情信息中的關(guān)注度指標(biāo)來(lái)映射現(xiàn)實(shí)交易行為,而對(duì)于網(wǎng)絡(luò)輿情信息和交易行為中隱含的內(nèi)容則表達(dá)不足,再加上學(xué)科理論交叉與信息技術(shù)的限制,使得網(wǎng)絡(luò)輿情信息與現(xiàn)實(shí)交易行為的映射關(guān)系雖被屢屢提及卻鮮有實(shí)證研究。為了解決上述問(wèn)題,本文以金融市場(chǎng)為背景選取現(xiàn)實(shí)的股票交易行為數(shù)據(jù)以及與股票相關(guān)聯(lián)的網(wǎng)絡(luò)輿情信息作為研究對(duì)象,分別基于網(wǎng)絡(luò)輿情信息的事件影響力,現(xiàn)實(shí)交易行為的時(shí)間序列分割和異常發(fā)現(xiàn)來(lái)研究網(wǎng)絡(luò)輿情信息與現(xiàn)實(shí)交易行為的映射關(guān)系。一方面,首先在網(wǎng)絡(luò)輿情信息中發(fā)現(xiàn)事件并在各個(gè)特定領(lǐng)域進(jìn)行追蹤,接著對(duì)事件按照時(shí)間粒度劃分,并給出事件影響力的測(cè)度方法,把事件影響力轉(zhuǎn)化為時(shí)間序列形式,最終在基于事件影響力的基礎(chǔ)上來(lái)發(fā)現(xiàn)網(wǎng)絡(luò)輿情信息與現(xiàn)實(shí)交易行為之間的映射關(guān)系;另一方面,對(duì)現(xiàn)實(shí)交易行為中的時(shí)間序列進(jìn)行分割和異常獲取,在基于時(shí)序分割與異常的基礎(chǔ)上來(lái)發(fā)現(xiàn)現(xiàn)實(shí)交易行為與網(wǎng)絡(luò)輿情信息的映射關(guān)系,并通過(guò)與網(wǎng)絡(luò)輿情信息的映射關(guān)系來(lái)發(fā)現(xiàn)交易行為產(chǎn)生這些規(guī)律和異常的原因。論文研究的主要內(nèi)容和創(chuàng)新點(diǎn)如下:(1)針對(duì)在特定領(lǐng)域進(jìn)行事件追蹤會(huì)帶來(lái)大量噪聲這一問(wèn)題,提出了一種基于帶權(quán)的最大二分圖匹配方法來(lái)追蹤特定領(lǐng)域中的事件,該方法利用關(guān)聯(lián)規(guī)則來(lái)限制部分關(guān)鍵詞的權(quán)重,提高了在特定領(lǐng)域追蹤事件時(shí)的抗噪音能力。(2)在事件影響力的基礎(chǔ)上研究網(wǎng)絡(luò)輿情信息與現(xiàn)實(shí)交易行為的映射關(guān)系。提出了一種結(jié)合事件熱度和參與事件傳播的用戶影響力來(lái)測(cè)度事件影響力的方法。該方法避免了由虛假熱度或者垃圾用戶導(dǎo)致的不真實(shí)的影響力結(jié)果,并把事件影響力按時(shí)間粒度劃分為時(shí)間序列形式,并在基于影響力的基礎(chǔ)上利用時(shí)間相關(guān)性和空間一致性構(gòu)建和分析網(wǎng)絡(luò)輿情信息與現(xiàn)實(shí)交易行為的映射關(guān)系。(3)在時(shí)間序列分割的基礎(chǔ)上研究現(xiàn)實(shí)交易行為與網(wǎng)絡(luò)輿情信息的映射關(guān)系。提出一種依據(jù)上下文關(guān)系的邊相似度方法來(lái)分割現(xiàn)實(shí)交易行為的時(shí)間序列。該算法克服傳統(tǒng)模式發(fā)現(xiàn)與模式匹配中的機(jī)械性,對(duì)現(xiàn)實(shí)交易行為產(chǎn)生的時(shí)間序列有更強(qiáng)的適應(yīng)能力。實(shí)驗(yàn)結(jié)果表明算法降低了孤立地考慮模式匹配導(dǎo)致的失效劃分,在嘈雜環(huán)境下具有更好的抗干擾性,更準(zhǔn)確的找出網(wǎng)絡(luò)輿情信息與現(xiàn)實(shí)交易行為的映射關(guān)系。(4)在時(shí)間序列異常的基礎(chǔ)上研究現(xiàn)實(shí)交易行為與網(wǎng)絡(luò)輿情信息的映射關(guān)系。針對(duì)現(xiàn)實(shí)交易行為的時(shí)變性與不可預(yù)測(cè)性,提出一種基于自適應(yīng)區(qū)間的異常捕捉方法。該方法依據(jù)數(shù)據(jù)本身特點(diǎn),用分離度度量數(shù)據(jù)之間的關(guān)系,并基于分離度構(gòu)建自適應(yīng)區(qū)間,接著利用自適應(yīng)區(qū)間過(guò)濾出異常數(shù)據(jù),最終在基于時(shí)間序列異常的基礎(chǔ)上構(gòu)建和分析網(wǎng)絡(luò)輿情信息與現(xiàn)實(shí)交易行為的映射關(guān)系。實(shí)驗(yàn)結(jié)果表明本文方法不僅能夠有效的發(fā)現(xiàn)時(shí)間序列的異常,而且可以有效的找到網(wǎng)絡(luò)輿情信息與現(xiàn)實(shí)交易行為的映射關(guān)系,幫助用戶發(fā)現(xiàn)產(chǎn)生異常的原因。
[Abstract]:With the rapid development of the Internet and social media, network public opinion information and the reality of trading behavior is becoming more and more highly integrated trend. The Internet from a simple information technology platform evolution has become a major carrier of social media, become an interactive information dissemination, sharing, social network communication and collaboration, great change people observe the social and economical way. With the participation of constantly improve, people are no longer passive acceptance of knowledge, but actively express a variety of opinions and comments on these views and comments not only can express people's real thoughts, but also can affect the audience's psychological reality and change the world. Now, researchers mainly use the network public opinion information of the attention index to map the real transactions, and for in network public opinion information and transactions With the content of expression, coupled with cross discipline theory and information technology, makes the mapping of network public opinion information and real transaction behavior was repeatedly mentioned but few empirical research. In order to solve the above problems, based on the market background of selected stock trading behavior and stock data reality and related information of network public opinion contact as the research object, based on network public opinion information influence events respectively, the mapping relationship between time series segmentation the reality of the trading behavior and abnormal findings of network public opinion information and practical transactions. On the one hand, first discovered and tracked in each specific event in the field of network public opinion information, then the event according to the time granularity and, given the influence of event measurement methods, the event influence into time series in the final form, based on events The basis of the mapping relation between the discovery of network public opinion information and the reality of trading behavior; on the other hand, the time series of real transaction behavior in segmentation and anomaly acquisition, based on temporal segmentation and abnormal up to find the mapping between the reality of trading behavior and network public opinion based on information, and with the network public opinion information mapping to find these trading rules and abnormal reasons. The main contents and innovations of this paper are as follows: (1) for the event tracking in specific areas will bring a lot of noise in this problem, a method is proposed for matching with the right to the maximum two points to track map based on domain specific events in weight the method uses association rules to limit some of the words, to improve the tracking of events in certain areas of the anti noise ability. (2) research network based on the influence of the event The mapping relationship between network public opinion information and practical transactions. A combination of heat events and participating in the event dissemination of user influence to measure the influence events. This method avoids the waste heat by false or untrue due to user influence results, and the influence of events according to the time granularity for time series, and the influence on the basis of using time correlation and spatial consistency of the construction and analysis of network public opinion information and real transactions based on the mapping. (3) the mapping relationship of reality in the transaction based on time series segmentation and network public opinion information. Time sequence based edge similarity method according to the realistic context of the segmentation trading behavior. The algorithm overcomes the traditional mode of discovery and mechanical in pattern matching, the real transaction time sequence Have a stronger ability to adapt to the column. The experimental results show that the algorithm reduces the consideration of pattern matching led to the failure of the division, has better anti-interference in noisy environments, mapping relationship more accurately find out the network public opinion information and the reality of trading behavior. (4) the mapping relationship of reality transactions based on time series anomaly with the network public opinion information. Based on the characteristic of real transaction behavior and unpredictability, this paper proposes a novel method for adaptive capture based on interval. The method according to the characteristics of the data itself, the relationship between the measurement data with the degree of separation and separation based on Constructing adaptive interval, then the adaptive interval filter out the abnormal data finally, in the foundation of time series anomaly and analysis of network public opinion information and real transactions based on the mapping. The experiment results show that this method is not It can only effectively discover the abnormity of time series, and it can effectively find the mapping relationship between network public opinion information and real transaction behavior, and help users find the cause of abnormal occurrence.

【學(xué)位授予單位】：上海大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：F830.91;TP391.1

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1 王蕾;網(wǎng)絡(luò)輿情信息與現(xiàn)實(shí)交易行為的映射關(guān)系研究與應(yīng)用[D];上海大學(xué);2016年

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本文編號(hào)：1381333