Zuccala, A.; Someren, M. van; Bellen, M. van: ¬A machine-learning approach to coding book reviews as quality indicators : toward a theory of megacitation (2014)
0.04
0.044135407 = sum of:
0.031801686 = product of:
0.12720674 = sum of:
0.12720674 = weight(_text_:author's in 1530) [ClassicSimilarity], result of:
0.12720674 = score(doc=1530,freq=2.0), product of:
0.3426541 = queryWeight, product of:
6.7201533 = idf(docFreq=144, maxDocs=44218)
0.05098903 = queryNorm
0.3712395 = fieldWeight in 1530, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
6.7201533 = idf(docFreq=144, maxDocs=44218)
0.0390625 = fieldNorm(doc=1530)
0.25 = coord(1/4)
0.012333722 = product of:
0.024667444 = sum of:
0.024667444 = weight(_text_:m in 1530) [ClassicSimilarity], result of:
0.024667444 = score(doc=1530,freq=4.0), product of:
0.12688358 = queryWeight, product of:
2.4884486 = idf(docFreq=9980, maxDocs=44218)
0.05098903 = queryNorm
0.19441006 = fieldWeight in 1530, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
2.4884486 = idf(docFreq=9980, maxDocs=44218)
0.0390625 = fieldNorm(doc=1530)
0.5 = coord(1/2)
- Abstract
- A theory of "megacitation" is introduced and used in an experiment to demonstrate how a qualitative scholarly book review can be converted into a weighted bibliometric indicator. We employ a manual human-coding approach to classify book reviews in the field of history based on reviewers' assessments of a book author's scholarly credibility (SC) and writing style (WS). In total, 100 book reviews were selected from the American Historical Review and coded for their positive/negative valence on these two dimensions. Most were coded as positive (68% for SC and 47% for WS), and there was also a small positive correlation between SC and WS (r = 0.2). We then constructed a classifier, combining both manual design and machine learning, to categorize sentiment-based sentences in history book reviews. The machine classifier produced a matched accuracy (matched to the human coding) of approximately 75% for SC and 64% for WS. WS was found to be more difficult to classify by machine than SC because of the reviewers' use of more subtle language. With further training data, a machine-learning approach could be useful for automatically classifying a large number of history book reviews at once. Weighted megacitations can be especially valuable if they are used in conjunction with regular book/journal citations, and "libcitations" (i.e., library holding counts) for a comprehensive assessment of a book/monograph's scholarly impact.
