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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
Structural Criteria
Formal Criteria
File
Import a single file...
Prepare a Corpus (Corpus Builder)
Open an existing project
Settings
Automatic and Customized Settings
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
Other Tools
Variable Manager
Advanced Corpus Search
Contingency Tables
Editor
Glossary
Analysis Unit
Association Indexes
Chi-Square
Cluster Analysis
Coding
Context Unit
Corpus and Subsets
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

Correspondence Analysis


Correspondence Analysis is a factorial analysis technique applied to the study of data tables whose cells contain either frequency values (real positive numbers) or presence-absence values ("1" or "0").

Like all factorial analysis techniques, correspondence analysis allows the extraction of new variables - the factors - with the property of summarizing in a organized way the significant information contained in the countless data tables cells; furthermore, this analysis technique allows the creation of graphs showing - in one or more spaces - the points that detect the objects in rows and columns, that is - in our case - the linguistic entities (words, lemmas, texts segments and texts) with the respective source features.

In geometrical terms, each factor sets up a spatial dimension - that can be represented as an axis line - whose center (or barycentre) is the value "0", and that develops in a bipolar way towards the negative (-) and positive (+) end, so that the objects put on opposite poles are the most different, almost like the "left" wing and the "right" wing on the political axes.

In T-LAB the analysis results are summarized through graphs that allow the evaluation of the relationships of proximity/distance - or rather similarity/dissimilarity - between the considered objects.

Furthermore, T-LAB shows measures (i.e. Absolute Contributions and Test Value) that help to understand the poles of factors that set up similarities/dissimilarities between the considered objects.