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T-LAB
Introduction
What T-LAB does and what it enables us to do
Requirements and Performances
Corpus Preparation
Corpus Preparation
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Formal Criteria
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Dictionary Building
Co-occurrence Analysis
Word Associations
Co-Word Analysis and Concept Mapping
Comparison between Word pairs
Sequence and Network Analysis
Concordances
Thematic Analysis
Thematic Analysis of Elementary Contexts
Modeling of Emerging Themes
Thematic Document Classification
Dictionary-Based Classification
Key Contexts of Thematic Words
Comparative Analysis
Specificity Analysis
Correspondence Analysis
Multiple Correspondence Analysis
Cluster Analysis
Singular Value Decomposition
Lexical Tools
Text Screening / Disambiguations
Corpus Vocabulary
Stop-Word List
Multi-Word List
Word Segmentation
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Variable Manager
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Glossary
Analysis Unit
Association Indexes
Chi-Square
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Coding
Context Unit
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Correspondence Analysis
Data Table
Disambiguation
Dictionary
Elementary Context
Frequency Threshold
Graph Maker
Homograph
IDnumber
Isotopy
Key-Word (Key-Term)
Lemmatization
Lexical Unit
Lexie and Lexicalization
Markov Chain
MDS
Multiwords
N-grams
Naïve Bayes
Normalization
Occurrences and Co-occurrences
Poles of Factors
Primary Document
Profile
Specificity
Stop Word List
Test Value
Thematic Nucleus
TF-IDF
Variables and Categories
Words and Lemmas
Bibliography
www.tlab.it

Multidimensional Scaling (MDS)


MDS is a set of data analysis techniques that allow us to analyse similarity matrices in order to provide a visual representation of the relationships among the data within a space of reduced dimensions.

T-LAB uses a type of MDS (Sammon's method) in order to represent the relationships among the lexical units or among the thematic nuclei (see Co-Word Analysis and Modeling of Emerging Themes).

The input tables are constituted by square matrices which contain proximity values (dissimilarities) derived from the calculation of an association index.

The results obtained, like those of the correspondence analysis, allow us to interpret both the relationships between the "objects" and the dimensions that organize the space in which they are represented.

The degree of correspondence between the distances among points implied by the MDS map and the matrix input is measured (inversely) by a Stress function. The lesser the stress value (e.g. < 0.10), the greater the goodness of the obtained adjustment.


The stress formula (Sammon's method) is the following:

Where stands for the distance between two points (ij) within the input matrix and stands for the distance between the same points (ij) within Sammon's map.