Textual data science using R

213 Pages·2018·31.515 MB·English

by  Bécue-Bertaut, Monica

Textual Data Science
with R
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Textual Data Science with R
Mónica Bécue-Bertaut

Textual Data Science
with R
Mónica Bécue-Bertaut
CRC Press
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Contents
Foreword xiii
Preface xv
1 Encoding: from a corpus to statistical tables 1
1.1 Textual and contextual data . . . . . . . . . . . . . . . . . . 1
1.1.1 Textual data . . . . . . . . . . . . . . . . . . . . . . . 1
1.1.2 Contextual data . . . . . . . . . . . . . . . . . . . . . 2
1.1.3 Documents and aggregate documents . . . . . . . . . 2
1.2 Examples and notation . . . . . . . . . . . . . . . . . . . . . 3
1.3 Choosing textual units . . . . . . . . . . . . . . . . . . . . . 5
1.3.1 Graphical forms . . . . . . . . . . . . . . . . . . . . . 6
1.3.2 Lemmas . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3.3 Stems . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.3.4 Repeated segments . . . . . . . . . . . . . . . . . . . . 7
1.3.5 In practice . . . . . . . . . . . . . . . . . . . . . . . . 7
1.4 Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.4.1 Unique spelling . . . . . . . . . . . . . . . . . . . . . . 9
1.4.2 Partially automated preprocessing . . . . . . . . . . . 9
1.4.3 Word selection . . . . . . . . . . . . . . . . . . . . . . 10
1.5 Word and segment indexes . . . . . . . . . . . . . . . . . . . 10
1.6 The Life UK corpus: preliminary results . . . . . . . . . . . 10
1.6.1 Verbal content through word and repeated segment
indexes . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.6.2 Univariate description of contextual variables . . . . . 13
1.6.3 A note on the frequency range . . . . . . . . . . . . . 13
1.7 Implementation with Xplortext . . . . . . . . . . . . . . . . 14
1.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2 Correspondence analysis of textual data 17
2.1 Data and goals . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.1.1 Correspondence analysis: a tool for linguistic data
analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.1.2 Data: a small example . . . . . . . . . . . . . . . . . . 17
2.1.3 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.2 Associations between documents and words . . . . . . . . . . 19
vii
viii Contents
2.2.1 Profile comparisons. . . . . . . . . . . . . . . . . . . . 19
2.2.2 Independence of documents and words . . . . . . . . . 20
2.2.3 The χ2 test . . . . . . . . . . . . . . . . . . . . . . . . 22
2.2.4 Association rates between documents and words . . . 23
2.3 Active row and column clouds . . . . . . . . . . . . . . . . . 24
2.3.1 Row and column profile spaces . . . . . . . . . . . . . 24
2.3.2 Distributional equivalence and the χ2 distance . . . . 24
2.3.3 Inertia of a cloud . . . . . . . . . . . . . . . . . . . . . 25
2.4 Fitting document and word clouds . . . . . . . . . . . . . . . 26
2.4.1 Factorial axes . . . . . . . . . . . . . . . . . . . . . . . 26
2.4.2 Visualizing rows and columns . . . . . . . . . . . . . . 28
2.4.2.1 Category representation . . . . . . . . . . . . 30
2.4.2.2 Word representation . . . . . . . . . . . . . . 30
2.4.2.3 Transition formulas . . . . . . . . . . . . . . 32
2.4.2.4 Simultaneous representation of rows and
columns . . . . . . . . . . . . . . . . . . . . . 32
2.5 Interpretation aids . . . . . . . . . . . . . . . . . . . . . . . . 32
2.5.1 Eigenvalues and representation quality of the clouds . 33
2.5.2 Contribution of documents and words to axis inertia . 34
2.5.3 Representation quality of a point . . . . . . . . . . . . 35
2.6 Supplementary rows and columns . . . . . . . . . . . . . . . 36
2.6.1 Supplementary tables . . . . . . . . . . . . . . . . . . 36
2.6.2 Supplementary frequency rows and columns . . . . . . 36
2.6.3 Supplementary quantitative and qualitative variables . 37
2.7 Validating the visualization . . . . . . . . . . . . . . . . . . . 37
2.8 Interpretation scheme for textual CA results . . . . . . . . . 38
2.9 Implementation with Xplortext . . . . . . . . . . . . . . . . 41
2.10 Summary of the CA approach . . . . . . . . . . . . . . . . . 41
3 Applications of correspondence analysis 43
3.1 Choosing the level of detail for analyses . . . . . . . . . . . . 43
3.2 Correspondence analysis on aggregate free text answers . . . 44
3.2.1 Data and objectives . . . . . . . . . . . . . . . . . . . 44
3.2.2 Word selection . . . . . . . . . . . . . . . . . . . . . . 44
3.2.3 CA on the aggregate table . . . . . . . . . . . . . . . . 44
3.2.3.1 Document representation . . . . . . . . . . . 45
3.2.3.2 Word representation . . . . . . . . . . . . . . 46
3.2.3.3 Simultaneous interpretation of the plots . . . 46
3.2.4 Supplementary elements . . . . . . . . . . . . . . . . . 49
3.2.4.1 Supplementary words . . . . . . . . . . . . . 49
3.2.4.2 Supplementary repeated segments . . . . . . 49
3.2.4.3 Supplementary categories . . . . . . . . . . . 50
3.2.5 Implementation with Xplortext . . . . . . . . . . . . . 51
3.3 Direct analysis . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.3.1 Data and objectives . . . . . . . . . . . . . . . . . . . 52
Contents ix
3.3.2 The main features of direct analysis . . . . . . . . . . 53
3.3.3 Direct analysis of the culture question . . . . . . . . . 53
3.3.4 Implementation with Xplortext . . . . . . . . . . . . . 58
4 Clustering in textual data science 61
4.1 Clustering documents . . . . . . . . . . . . . . . . . . . . . . 61
4.2 Dissimilarity measures between documents . . . . . . . . . . 62
4.3 Measuring partition quality . . . . . . . . . . . . . . . . . . . 63
4.3.1 Document clusters in the factorial space . . . . . . . . 63
4.3.2 Partition quality . . . . . . . . . . . . . . . . . . . . . 63
4.4 Dissimilarity measures between document clusters . . . . . . 64
4.4.1 The single-linkage method . . . . . . . . . . . . . . . . 64
4.4.2 The complete-linkage method . . . . . . . . . . . . . . 64
4.4.3 Ward’s method . . . . . . . . . . . . . . . . . . . . . . 64
4.5 Agglomerative hierarchical clustering . . . . . . . . . . . . . 65
4.5.1 Hierarchical tree construction algorithm . . . . . . . . 65
4.5.2 Selecting the final partition . . . . . . . . . . . . . . . 66
4.5.3 Interpreting clusters . . . . . . . . . . . . . . . . . . . 66
4.6 Direct partitioning . . . . . . . . . . . . . . . . . . . . . . . . 67
4.7 Combining clustering methods . . . . . . . . . . . . . . . . . 68
4.7.1 Consolidating partitions . . . . . . . . . . . . . . . . . 68
4.7.2 Direct partitioning followed by AHC . . . . . . . . . . 68
4.8 A procedure for combining CA and clustering . . . . . . . . 69
4.9 Example: joint use of CA and AHC . . . . . . . . . . . . . . 69
4.9.1 Data and objectives . . . . . . . . . . . . . . . . . . . 69
4.9.1.1 Data preprocessing using CA . . . . . . . . . 70
4.9.1.2 Constructing the hierarchical tree . . . . . . 70
4.9.1.3 Choosing the final partition . . . . . . . . . . 72
4.10 Contiguity-constrained hierarchical clustering . . . . . . . . . 74
4.10.1 Principles and algorithm . . . . . . . . . . . . . . . . . 74
4.10.2 AHC of age groups with a chronological constraint . . 75
4.10.3 Implementation with Xplortext . . . . . . . . . . . . . 76
4.11 Example: clustering free text answers . . . . . . . . . . . . . 76
4.11.1 Data and objectives . . . . . . . . . . . . . . . . . . . 76
4.11.2 Data preprocessing . . . . . . . . . . . . . . . . . . . . 78
4.11.2.1 CA: eigenvalues and total inertia . . . . . . . 78
4.11.2.2 Interpreting the first axes . . . . . . . . . . . 80
4.11.3 AHC: building the tree and choosing the final partition 84
4.12 Describing cluster features . . . . . . . . . . . . . . . . . . . 88
4.12.1 Lexical features of clusters. . . . . . . . . . . . . . . . 89
4.12.1.1 Describing clusters in terms of characteristic
words . . . . . . . . . . . . . . . . . . . . . . 89
4.12.1.2 Describing clusters in terms of characteristic
documents . . . . . . . . . . . . . . . . . . . 91
4.12.2 Describing clusters using contextual variables . . . . . 91