Search (4 results, page 1 of 1)

0.027485436 = product of:
  0.05497087 = sum of:
    0.05497087 = product of:
      0.10994174 = sum of:
        0.10994174 = weight(_text_:tagging in 2671) [ClassicSimilarity], result of:
          0.10994174 = score(doc=2671,freq=4.0), product of:
            0.2979515 = queryWeight, product of:
              5.9038734 = idf(docFreq=327, maxDocs=44218)
              0.05046712 = queryNorm
            0.3689921 = fieldWeight in 2671, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              5.9038734 = idf(docFreq=327, maxDocs=44218)
              0.03125 = fieldNorm(doc=2671)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

In recent years, there has been a rapid growth of user-generated data in collaborative tagging (a.k.a. folksonomy-based) systems due to the prevailing of Web 2.0 communities. To effectively assist users to find their desired resources, it is critical to understand user behaviors and preferences. Tag-based profile techniques, which model users and resources by a vector of relevant tags, are widely employed in folksonomy-based systems. This is mainly because that personalized search and recommendations can be facilitated by measuring relevance between user profiles and resource profiles. However, conventional measurements neglect the sentiment aspect of user-generated tags. In fact, tags can be very emotional and subjective, as users usually express their perceptions and feelings about the resources by tags. Therefore, it is necessary to take sentiment relevance into account into measurements. In this paper, we present a novel generic framework SenticRank to incorporate various sentiment information to various sentiment-based information for personalized search by user profiles and resource profiles. In this framework, content-based sentiment ranking and collaborative sentiment ranking methods are proposed to obtain sentiment-based personalized ranking. To the best of our knowledge, this is the first work of integrating sentiment information to address the problem of the personalized tag-based search in collaborative tagging systems. Moreover, we compare the proposed sentiment-based personalized search with baselines in the experiments, the results of which have verified the effectiveness of the proposed framework. In addition, we study the influences by popular sentiment dictionaries, and SenticNet is the most prominent knowledge base to boost the performance of personalized search in folksonomy.

0.024293922 = product of:
  0.048587844 = sum of:
    0.048587844 = product of:
      0.09717569 = sum of:
        0.09717569 = weight(_text_:tagging in 5310) [ClassicSimilarity], result of:
          0.09717569 = score(doc=5310,freq=2.0), product of:
            0.2979515 = queryWeight, product of:
              5.9038734 = idf(docFreq=327, maxDocs=44218)
              0.05046712 = queryNorm
            0.326146 = fieldWeight in 5310, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.9038734 = idf(docFreq=327, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5310)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The novelty of knowledge claims in a research paper can be considered an evaluation criterion for papers to supplement citations. To provide a foundation for research evaluation from the perspective of innovativeness, we propose an automatic approach for extracting innovative ideas from the abstracts of technology and engineering papers. The approach extracts N-grams as candidates based on part-of-speech tagging and determines whether they are novel by checking the Scopus® database to determine whether they had ever been presented previously. Moreover, we discussed the distributions of innovative ideas in different abstract structures. To improve the performance by excluding noisy N-grams, a list of stopwords and a list of research description characteristics were developed. We selected abstracts of articles published from 2011 to 2017 with the topic of semantic analysis as the experimental texts. Excluding noisy N-grams, considering the distribution of innovative ideas in abstracts, and suitably combining N-grams can effectively improve the performance of automatic innovative idea extraction. Unlike co-word and co-citation analysis, innovative-idea extraction aims to identify the differences in a paper from all previously published papers.

0.010256397 = product of:
  0.020512793 = sum of:
    0.020512793 = product of:
      0.041025586 = sum of:
        0.041025586 = weight(_text_:22 in 5292) [ClassicSimilarity], result of:
          0.041025586 = score(doc=5292,freq=2.0), product of:
            0.17672725 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05046712 = queryNorm
            0.23214069 = fieldWeight in 5292, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=5292)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

22. 7.2006 17:33:43

0.010256397 = product of:
  0.020512793 = sum of:
    0.020512793 = product of:
      0.041025586 = sum of:
        0.041025586 = weight(_text_:22 in 5101) [ClassicSimilarity], result of:
          0.041025586 = score(doc=5101,freq=2.0), product of:
            0.17672725 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05046712 = queryNorm
            0.23214069 = fieldWeight in 5101, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=5101)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

17. 3.2019 13:22:53