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This paper presents a novel method to track the hierarchical structure of Web video groups on the basis of salient keyword matching including semantic broadness estimation. To the best of our knowledge, this paper is the first work to perform extraction and tracking of the hierarchical structure simultaneously
challenging research work. This paper proposes a method LET(LDA&Entropy&Tex-trank) to extract topic keywords from Sina Weibo topics text sets. LET considers both topic influence of keywords and topic discrimination of keyword that combines the merits of LDA, Entropy and TextRank. In addition, we design a new standard
apps via a set of keywords. MARK then lists the reviews most relevant to those keywords for further analyses. It can also draw the trends over time of the selected keywords, which might help the analyst to detect sudden changes in the related user reviews. To help the analyst describe her interests more effectively, MARK
Criminal Intelligence Analysis often requires a search different from the semantic and keyword based searching to reveal the associations among semantically and operationally connected objects within a crime knowledge base. In this paper we introduce associative search as a search along the networks of association
) specific to emotions and story genres and (iv) synthesis of story speech using mark-up language and prosody modification factors. Keyword and part-of-speech (POS) features are used for story-genre classification and emotion prediction. The prosody modification factors are derived carefully by analyzing the perceptual
quality of life. This article considers past, present, and future trends of sentiment analysis by delving into the evolution of different tools and techniques—from heuristics to discourse structure, from coarse- to fine-grained analysis, and from keyword- to concept-level opinion mining.
always ignores relativity of the topic. These affect the topic discovery and topic trend. Therefore, combining with the keywords combination and Word2Vec model to strength expression of semantic information in topic clustering, this article sets weighted K-means algorithm for topic discovery. The results show our weighted K
Candidates routinely use a set of key phrases or keywords to succinctly describe their expertise or skillset. This is useful for both matching candidate profiles to jobs and for comparing different candidates. Constant development of businesses and labour market has dynamic impact on importance of such skills, where
used several patent databases such as USPTO, MyIPO, WIPO, and EPO to collect patents. In patent searching, we used keywords, which were derived from Islamic Finance and Banking phrase, book's title, of which its content is related to Islamic Finance and Banking, and all concepts on Islamic Finance and Banking ontology
With a growing number of Web documents, many approaches have been proposed for knowledge discovery on Web documents. The documents do not always provide keywords or categories, so unsupervised approaches are desirable, and topic modeling is such an approach for knowledge discovery without using labels. Further, Web
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