本文選題：熱度矩陣 + 主題模型　； 參考：《計(jì)算機(jī)工程》2017年02期

【摘要】：現(xiàn)有微博熱點(diǎn)話題發(fā)現(xiàn)模型對微博數(shù)量規(guī)模較敏感,發(fā)現(xiàn)速度較慢。為此,提出一種基于熱度矩陣的主題模型。通過熱度矩陣獲取各潛在主題的熱度和主題-詞概率分布,并以詞間的共有熱度來挖掘其語義關(guān)系,進(jìn)而準(zhǔn)確識別數(shù)據(jù)中的熱點(diǎn)話題及熱點(diǎn)詞匯。在真實(shí)微博數(shù)據(jù)上的實(shí)驗(yàn)結(jié)果表明,與潛在狄利克雷分布模型相比,該模型的效率和準(zhǔn)確率較高,發(fā)現(xiàn)的熱點(diǎn)話題與實(shí)時(shí)事件保持一致,具有較好的熱點(diǎn)識別效果。
[Abstract]:The current hot topic discovery model of Weibo is sensitive to the quantity and scale of Weibo, and the speed of discovery is slow. Therefore, a topic model based on heat matrix is proposed. The heat intensity and topic-word probability distribution of each potential topic are obtained by the heat matrix, and the semantic relationship is mined by the common heat between words, and then the hot topics and hot words in the data can be accurately identified. The experimental results on the real Weibo data show that the proposed model is more efficient and accurate than the potential Drickley distribution model, and the hot topics detected are consistent with the real-time events and have a better hot spot recognition effect.
【作者單位】： 武漢大學(xué)軟件工程國家重點(diǎn)實(shí)驗(yàn)室;中國電子科技集團(tuán)公司第二十八研究所;
【基金】：國家自然科學(xué)基金重點(diǎn)項(xiàng)目(U1135005)
【分類號】：TP391.1

【參考文獻(xiàn)】

相關(guān)期刊論文 前5條

1 李敬;印鑒;劉少鵬;潘雅麗;;基于話題標(biāo)簽的微博主題挖掘[J];計(jì)算機(jī)工程;2015年04期

2 孫永利;李東;張s，

本文編號：1859985