Search (6 results, page 1 of 1)

0.01884501 = product of:
  0.03769002 = sum of:
    0.03769002 = product of:
      0.15076008 = sum of:
        0.15076008 = weight(_text_:author's in 320) [ClassicSimilarity], result of:
          0.15076008 = score(doc=320,freq=2.0), product of:
            0.338416 = queryWeight, product of:
              6.7201533 = idf(docFreq=144, maxDocs=44218)
              0.050358377 = queryNorm
            0.44548744 = fieldWeight in 320, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              6.7201533 = idf(docFreq=144, maxDocs=44218)
              0.046875 = fieldNorm(doc=320)
      0.25 = coord(1/4)
  0.5 = coord(1/2)

Abstract

This article argues that authorial stance should be taken into account in the indexing of scientific documents. Authorial stance has been widely studied in linguistics and is a typical feature of scientific writing that reveals the uniqueness of each author's perspective, their scientific contribution, and their thinking. We argue that authorial stance guides the reading of scientific documents and that it can be used to characterize the knowledge contained in such documents. Our research has previously shown that people reading dissertations are interested both in a topic and in a document's authorial stance. Now, we would like to propose a two-tiered indexing system. Dissertations would first be divided into paragraphs; then, each information unit would be defined by topic and by the markers of authorial stance present in the document.

0.017678065 = product of:
  0.03535613 = sum of:
    0.03535613 = product of:
      0.07071226 = sum of:
        0.07071226 = weight(_text_:f in 4707) [ClassicSimilarity], result of:
          0.07071226 = score(doc=4707,freq=2.0), product of:
            0.20071772 = queryWeight, product of:
              3.985786 = idf(docFreq=2232, maxDocs=44218)
              0.050358377 = queryNorm
            0.35229704 = fieldWeight in 4707, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.985786 = idf(docFreq=2232, maxDocs=44218)
              0.0625 = fieldNorm(doc=4707)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Source

Challenges and opportunities for knowledge organization in the digital age: proceedings of the Fifteenth International ISKO Conference, 9-11 July 2018, Porto, Portugal / organized by: International Society for Knowledge Organization (ISKO), ISKO Spain and Portugal Chapter, University of Porto - Faculty of Arts and Humanities, Research Centre in Communication, Information and Digital Culture (CIC.digital) - Porto. Eds.: F. Ribeiro u. M.E. Cerveira

0.015625348 = product of:
  0.031250697 = sum of:
    0.031250697 = product of:
      0.06250139 = sum of:
        0.06250139 = weight(_text_:f in 2667) [ClassicSimilarity], result of:
          0.06250139 = score(doc=2667,freq=4.0), product of:
            0.20071772 = queryWeight, product of:
              3.985786 = idf(docFreq=2232, maxDocs=44218)
              0.050358377 = queryNorm
            0.31138954 = fieldWeight in 2667, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.985786 = idf(docFreq=2232, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2667)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Sentiment analysis on Twitter has attracted much attention recently due to its wide applications in both, commercial and public sectors. In this paper we present SentiCircles, a lexicon-based approach for sentiment analysis on Twitter. Different from typical lexicon-based approaches, which offer a fixed and static prior sentiment polarities of words regardless of their context, SentiCircles takes into account the co-occurrence patterns of words in different contexts in tweets to capture their semantics and update their pre-assigned strength and polarity in sentiment lexicons accordingly. Our approach allows for the detection of sentiment at both entity-level and tweet-level. We evaluate our proposed approach on three Twitter datasets using three different sentiment lexicons to derive word prior sentiments. Results show that our approach significantly outperforms the baselines in accuracy and F-measure for entity-level subjectivity (neutral vs. polar) and polarity (positive vs. negative) detections. For tweet-level sentiment detection, our approach performs better than the state-of-the-art SentiStrength by 4-5% in accuracy in two datasets, but falls marginally behind by 1% in F-measure in the third dataset.

0.012061235 = product of:
  0.02412247 = sum of:
    0.02412247 = product of:
      0.04824494 = sum of:
        0.04824494 = weight(_text_:22 in 4888) [ClassicSimilarity], result of:
          0.04824494 = score(doc=4888,freq=4.0), product of:
            0.17634645 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050358377 = queryNorm
            0.27358043 = fieldWeight in 4888, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4888)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

22. 1.2012 13:02:10

Source

Knowledge organization. 39(2012) no.1, S.13-22

0.01104879 = product of:
  0.02209758 = sum of:
    0.02209758 = product of:
      0.04419516 = sum of:
        0.04419516 = weight(_text_:f in 2669) [ClassicSimilarity], result of:
          0.04419516 = score(doc=2669,freq=2.0), product of:
            0.20071772 = queryWeight, product of:
              3.985786 = idf(docFreq=2232, maxDocs=44218)
              0.050358377 = queryNorm
            0.22018565 = fieldWeight in 2669, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.985786 = idf(docFreq=2232, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2669)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

0.010234296 = product of:
  0.020468593 = sum of:
    0.020468593 = product of:
      0.040937185 = sum of:
        0.040937185 = weight(_text_:22 in 1416) [ClassicSimilarity], result of:
          0.040937185 = score(doc=1416,freq=2.0), product of:
            0.17634645 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050358377 = queryNorm
            0.23214069 = fieldWeight in 1416, 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=1416)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Source

Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik