Search (4 results, page 1 of 1)

0.12791318 = product of:
  0.17055091 = sum of:
    0.029088326 = weight(_text_:web in 4626) [ClassicSimilarity], result of:
      0.029088326 = score(doc=4626,freq=2.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.18028519 = fieldWeight in 4626, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4626)
    0.07377557 = weight(_text_:search in 4626) [ClassicSimilarity], result of:
      0.07377557 = score(doc=4626,freq=10.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.4293381 = fieldWeight in 4626, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4626)
    0.06768702 = product of:
      0.13537404 = sum of:
        0.13537404 = weight(_text_:engine in 4626) [ClassicSimilarity], result of:
          0.13537404 = score(doc=4626,freq=6.0), product of:
            0.26447627 = queryWeight, product of:
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.049439456 = queryNorm
            0.51185703 = fieldWeight in 4626, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              5.349498 = idf(docFreq=570, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4626)
      0.5 = coord(1/2)
  0.75 = coord(3/4)

Abstract

The primary webometric method for estimating the online impact of an organization is to count links to its website. Link counts have been available from commercial search engines for over a decade but this was set to end by early 2012 and so a replacement is needed. This article compares link counts to two alternative methods: URL citations and organization title mentions. New variations of these methods are also introduced. The three methods are compared against each other using Yahoo!. Two of the three methods (URL citations and organization title mentions) are also compared against each other using Bing. Evidence from a case study of 131 UK universities and 49 US Library and Information Science (LIS) departments suggests that Bing's Hit Count Estimates (HCEs) for popular title searches are not useful for webometric research but that Yahoo!'s HCEs for all three types of search and Bing's URL citation HCEs seem to be consistent. For exact URL counts the results of all three methods in Yahoo! and both methods in Bing are also consistent. Four types of accuracy factors are also introduced and defined: search engine coverage, search engine retrieval variation, search engine retrieval anomalies, and query polysemy.

0.05376438 = product of:
  0.10752876 = sum of:
    0.050382458 = weight(_text_:web in 4533) [ClassicSimilarity], result of:
      0.050382458 = score(doc=4533,freq=6.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.3122631 = fieldWeight in 4533, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4533)
    0.057146307 = weight(_text_:search in 4533) [ClassicSimilarity], result of:
      0.057146307 = score(doc=4533,freq=6.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.33256388 = fieldWeight in 4533, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4533)
  0.5 = coord(2/4)

Abstract

Purpose - Link analysis is an established topic within webometrics. It normally uses counts of links between sets of web sites or to sets of web sites. These link counts are derived from web crawlers or commercial search engines with the latter being the only alternative for some investigations. This paper compares link counts with URL citation counts in order to assess whether the latter could be a replacement for the former if the major search engines withdraw their advanced hyperlink search facilities. Design/methodology/approach - URL citation counts are compared with link counts for a variety of data sets used in previous webometric studies. Findings - The results show a high degree of correlation between the two but with URL citations being much less numerous, at least outside academia and business. Research limitations/implications - The results cover a small selection of 15 case studies and so the findings are only indicative. Significant differences between results indicate that the difference between link counts and URL citation counts will vary between webometric studies. Practical implications - Should link searches be withdrawn, then link analyses of less well linked non-academic, non-commercial sites would be seriously weakened, although citations based on e-mail addresses could help to make citations more numerous than links for some business and academic contexts. Originality/value - This is the first systematic study of the difference between link counts and URL citation counts in a variety of contexts and it shows that there are significant differences between the two.

0.017452994 = product of:
  0.06981198 = sum of:
    0.06981198 = weight(_text_:web in 1004) [ClassicSimilarity], result of:
      0.06981198 = score(doc=1004,freq=8.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.43268442 = fieldWeight in 1004, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=1004)
  0.25 = coord(1/4)

Abstract

General sentiment analysis for the social web has become increasingly useful for shedding light on the role of emotion in online communication and offline events in both academic research and data journalism. Nevertheless, existing general-purpose social web sentiment analysis algorithms may not be optimal for texts focussed around specific topics. This article introduces 2 new methods, mood setting and lexicon extension, to improve the accuracy of topic-specific lexical sentiment strength detection for the social web. Mood setting allows the topic mood to determine the default polarity for ostensibly neutral expressive text. Topic-specific lexicon extension involves adding topic-specific words to the default general sentiment lexicon. Experiments with 8 data sets show that both methods can improve sentiment analysis performance in corpora and are recommended when the topic focus is tightest.

0.0050237724 = product of:
  0.02009509 = sum of:
    0.02009509 = product of:
      0.04019018 = sum of:
        0.04019018 = weight(_text_:22 in 4345) [ClassicSimilarity], result of:
          0.04019018 = score(doc=4345,freq=2.0), product of:
            0.17312855 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.049439456 = queryNorm
            0.23214069 = fieldWeight in 4345, 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=4345)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

22. 1.2011 14:27:06