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E-raamat: Introduction to Text Visualization

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Introduction to Text VisualizationSuurem pilt
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This book provides a systematic review of many advanced techniques to support the analysis of large collections of documents, ranging  from the elementary to the profound, covering all the aspects of the visualization of text documents. Particularly, we start by introducing the fundamental concept of information visualization and visual analysis, followed by a brief survey of the field of text visualization and commonly used data models for converting document into a structured form for visualization. Then we introduce the key visualization techniques including visualizing document similarity, content, sentiments, as well as text corpus exploration system in details with concrete examples in the rest of the book.
1 Introduction
1(10)
1.1 Information Visualization
1(7)
1.2 Text Visualization
8(1)
1.3 Book Outline
9(2)
References
10(1)
2 Overview of Text Visualization Techniques
11(30)
2.1 Review Scope and Taxonomy
11(2)
2.2 Visualizing Document Similarity
13(3)
2.2.1 Projection Oriented Techniques
13(2)
2.2.2 Semantic Oriented Techniques
15(1)
2.3 Revealing Text Content
16(12)
2.3.1 Summarizing a Single Document
16(2)
2.3.2 Showing Content at the Word Level
18(3)
2.3.3 Visualizing Topics
21(3)
2.3.4 Showing Events and Storyline
24(4)
2.4 Visualizing Sentiments and Emotions
28(3)
2.5 Document Exploration Techniques
31(3)
2.5.1 Distortion Based Approaches
32(1)
2.5.2 Exploration Based on Document Similarity
32(1)
2.5.3 Hierarchical Document Exploration
33(1)
2.5.4 Search and Query Based Approaches
33(1)
2.6 Summary of the
Chapter
34(7)
References
35(6)
3 Data Model
41(8)
3.1 Data Structures at the Word Level
43(1)
3.1.1 Bag of Words and N-Gram
43(1)
3.1.2 Word Frequency Vector
43(1)
3.2 Data Structures at the Syntactical-Level
44(1)
3.3 Data Models at the Semantic Level
45(3)
3.3.1 Network Oriented Data Models
45(1)
3.3.2 Multifaceted Entity-Relational Data Model
46(2)
3.4 Summary of the
Chapter
48(1)
References
48(1)
4 Visualizing Document Similarity
49(8)
4.1 Projection Based Approaches
49(5)
4.1.1 Linear Projections
50(1)
4.1.2 Non-linear Projections
51(3)
4.2 Semantic Oriented Techniques
54(1)
4.3 Conclusion
55(2)
References
55(2)
5 Visualizing Document Content
57(46)
5.1 "What We Say": Word
58(16)
5.1.1 Frequency
59(8)
5.1.2 Frequency Trend
67(7)
5.2 "How We Say": Structure
74(10)
5.2.1 Co-occurrence Relationships
75(3)
5.2.2 Concordance Relationships
78(1)
5.2.3 Grammar Structure
79(3)
5.2.4 Repetition Relationships
82(2)
5.3 "What Can Be Inferred": Substance
84(12)
5.3.1 Fingerprint
84(3)
5.3.2 Topics
87(3)
5.3.3 Topic Evolutions
90(3)
5.3.4 Event
93(3)
5.4 Summary of the
Chapter
96(7)
References
97(6)
6 Visualizing Sentiments and Emotions
103
6.1 Introduction
103(4)
6.2 Visual Analysis of Customer Comments
107(2)
6.3 Visualizing Sentiment Diffusion
109(2)
6.4 Visualizing Sentiment Divergence in Social Media
111(2)
6.5 Conclusion
113
References
113
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