By Lei Zhang, Bing Liu (auth.), Wesley W. Chu (eds.)
The box of knowledge mining has made major and far-reaching advances over the last 3 many years. as a result of its strength energy for fixing complicated difficulties, info mining has been effectively utilized to assorted components corresponding to company, engineering, social media, and organic technological know-how. a lot of those purposes look for styles in advanced structural info. In biomedicine for instance, modeling complicated organic platforms calls for linking wisdom throughout many degrees of technology, from genes to sickness. extra, the information features of the issues have additionally grown from static to dynamic and spatiotemporal, entire to incomplete, and centralized to allotted, and develop of their scope and measurement (this is called big data). The powerful integration of massive facts for decision-making additionally calls for privateness renovation.
The contributions to this monograph summarize the advances of information mining within the respective fields. This quantity involves 9 chapters that tackle topics starting from mining info from opinion, spatiotemporal databases, discriminative subgraph styles, course wisdom discovery, social media, and privateness matters to the topic of computation aid through binary matrix factorization.
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Aspect and sentiment unification model for online review analysis. : Targeting sentiment expressions through supervised ranking of linguistic configurations. : Extracting opinions, opinion holders, and topics expressed in online news media text. : Authoritative sources in hyper-linked environment. : Extracting aspect-evaluation and aspect-of relations in opinion mining. : Opinion extraction, summarization and tracking in news and blog corpora. : Conditional random fields: probabilistic models for segmenting and labeling sequence data.
Identifying comparative sentences in text documents. : Aspect and sentiment unification model for online review analysis. : Targeting sentiment expressions through supervised ranking of linguistic configurations. : Extracting opinions, opinion holders, and topics expressed in online news media text. : Authoritative sources in hyper-linked environment. : Extracting aspect-evaluation and aspect-of relations in opinion mining. : Opinion extraction, summarization and tracking in news and blog corpora.
Aspect and Entity Extraction for Opinion Mining 21 types (aspect, opinion word, or background word). Moghaddam and Ester (2011) proposed a model called ILDA, which is based on LDA and jointly models latent aspects and rating. ILDA can be viewed as a generative process that first generates an aspect and subsequently generates its rating. In particular, for generating each opinion phrase, ILDA first generates an aspect am from an LDA model. Then it generates a rating rm conditioned on the sampled aspect am.