【摘要】：【目的/意義】隨著網(wǎng)絡(luò)社交媒體的發(fā)展,輿情文本中隱含的主題越來(lái)越能體現(xiàn)出人們的關(guān)注點(diǎn)所在及變化情況,因此對(duì)其進(jìn)行檢測(cè)及演化分析具有重要意義。【方法/過程】為了解決OLDA模型存在的主題混合及權(quán)重定義問題,本文提出了一種可變?cè)诰�LDA模型(variable online LDA,VOLDA),通過構(gòu)建主題相似度矩陣,明確主題變化關(guān)系,在主題內(nèi)容演化矩陣中剔除含有舊主題的時(shí)間片,從而構(gòu)建變長(zhǎng)的演化矩陣,并在此基礎(chǔ)上設(shè)計(jì)動(dòng)態(tài)權(quán)重計(jì)算方法及先驗(yàn)參數(shù)優(yōu)化方法�！窘Y(jié)果/結(jié)論】基于論壇文本數(shù)據(jù)的實(shí)驗(yàn)結(jié)果表明,VOLDA模型能夠有效減少新主題出現(xiàn)后的主題混合問題,并且提高主題在演化過程中的表示能力。
[Abstract]:[purpose / meaning] with the development of online social media, the topics implied in the text of public opinion increasingly reflect people's concerns and changes. Therefore, it is of great significance to detect and analyze its evolution. [method / process] in order to solve the problem of topic mixing and weight definition in OLDA model, In this paper, a variable online LDA model, (variable online LDA-VOLDA), is proposed. By constructing the topic similarity matrix, the relationship of topic variation is clarified, and the time slice containing the old theme is eliminated in the topic content evolution matrix, thus the variable length evolution matrix is constructed. On this basis, a dynamic weight calculation method and a priori parameter optimization method are designed. [results / conclusions] the experimental results based on the forum text data show that the VOLDA model can effectively reduce the topic mixing problem after the new topic appears. And improve the expression of topics in the evolution process.
【作者單位】： 南京航空航天大學(xué)經(jīng)濟(jì)與管理學(xué)院;
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(71373123) 江蘇高校哲學(xué)社會(huì)科學(xué)研究重點(diǎn)項(xiàng)目(2015ZDIXM007)
【分類號(hào)】：TP391.1
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本文編號(hào)：2174096