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What T-LAB does and what it enables us to do
Requirements and Performances
Corpus Preparation
Corpus Preparation
Structural Criteria
Formal Criteria
Import a single file...
Prepare a Corpus (Corpus Builder)
Open an existing project
Automatic and Customized Settings
Dictionary Building
Co-occurrence Analysis
Word Associations
Co-Word Analysis and Concept Mapping
Comparison between Word pairs
Sequence Analysis
Thematic Analysis
Thematic Analysis of Elementary Contexts
Thematic Document Classification
Dictionary-Based Classification
Modeling of Emerging Themes
Key Contexts of Thematic Words
Comparative Analysis
Specificity Analysis
Correspondence Analysis
Multiple Correspondence Analysis
Cluster Analysis
Contingency Tables
Lexical Tools
Text Screening / Disambiguations
Corpus Vocabulary
Stop-Word List
Multi-Word List
Word Segmentation
Other Tools
Variable Manager
Create a Sub-Corpus
Analysis Unit
Association Indexes
Cluster Analysis
Context Unit
Corpus and Subsets
Correspondence Analysis
Data Table
Elementary Context
Frequency Threshold
Graph Maker
Key-Word (Key-Term)
Lexical Unit
Lexie and Lexicalization
Markov Chain
Naïve Bayes
Occurrences and Co-occurrences
Poles of Factors
Primary Document
Stop Word List
Test Value
Thematic Nucleus
Variables and Categories
Words and Lemmas

Co-Word Analysis and Concept Mapping

N.B.: The pictures shown in this section have been obtained by using a previous version of T-LAB. These pictures look slightly different in T-LAB Plus. Also: a) when the 'automatic selection of key terms' is selected, different colours are used for different groups of items in the MDS map; b) there is a new button (Graph Maker) which allows the user to create several dynamic charts in HTML format; c) by right clicking on the keyword tables, additional options become available.

This T-LAB tool allows us to find and map two kinds of relationships concerning word co-occurrences:

A - between single key-words (lemmas or categories), if their number does not exceed 100 elements (min 10);
B - between/within little clusters (i.e. Thematic Nuclei), if the number of key-words selected exceeds 100 elements (max 3,000).

The user may choose which association index to be used and, for option B only, he may also choose both the maximum number of clusters to be obtained (from 50 to 100) and the maximum number of key-terms within each cluster.

The computation process includes the following steps:

1- building a co-occurrence matrix (word x word);
2- computing the selected association indexes (Cosine, Dice, Jaccard, Equivalence, Inclusion, Mutual Information);
3- hierarchical clustering of the dissimilarity matrix;
4- building a second dissimilarity matrix (cluster x cluster);

5- graphic representation by multidimensional scaling and correspondence analysis.

- in "A" cases ((see the below image), the user can review the key-term selection and T-LAB doesn't carry out steps 3 an 4;

- the quality of results depends on a thorough selection of key-words;
- as the multiwords unclassified by T-LAB are specific cases of co-occurrence and the "B" option treats them like little clusters (e.g. "Twin" + "Towers"), the user is advised to resolve these cases during the pre-processing phase. Anyway, without repeating the corpus importation, it is possible to make changes by means of the Dictionary Building function (e.g. by assigning the label "Twin_Towers" to the two different items "Twin" and "Towers");

- by clicking on the appropriate buttons all data tables can be checked (see the picture below).

When the automatic analysis is over, four kinds of charts are available (see below) and each of them can be customized by using the appropriate dialog box (just right click on the chart).

1 - MDS Map

2 - Factorial Analysis of Correspondences

3 - Association Diagram

4 - Diagram of Centrality-Density measures (after a cluster analysis only)

In particular, the results obtained by Correspondence Analysis can be mapped using the coordinates of the first ten axes (see "A" below).
As T-LAB allows us to verify the Test Values of each factor (see "B" below), this kind of output can be useful for an accurate interpretation of the relationships between cluster and/or key-words.

The charts can be explored and customized in the following ways:

click on a table item or on a chart point
diagram of corresponding associations
click on a label of "CLUSTER"column
(see "A" below)
list of cluster elements
click on "apply the new label" (see "B" below)
new label assigned to the cluster
click on "aggregation steps" (see "C" below)
word aggregation within the cluster
right click on the chart
open the dialog menu

A further option allows us to select the items (i.e. the 'nodes') for Network Analysis (see the image below, step 1 and 2), to export the corresponding adjacency matrix (step 3), select the links on the basis of their range of probability value (step 4) and export different types of files (step 5) which can be edited by software such as Gephi, Pajek, Ucinet, yEd and others.

N.B.: In T-LAB Plus the following window has been replaced by the Graph Maker tool.

There are available three tables which can be exported by this T-LAB tool:

1 - "Cluster Membership" table (see below) deals with the hierarchical aggregation of words within each cluster;

2 - "Summary" table (see below) includes the following measures:

- ECQ = Quantity of Elementary Contexts in which two or more word clusters are co-occurring;
- Centrality = average of association indexes concerning cluster relationships;
- Density = average of word association indexes within each cluster.

3 - "Association Indexes" table (see below) includes similarity measures of the between and the within cluster relationships.


- when a Cluster Analysis has not been carried out, the "Cluster Membership" table is not available, consequently the "Summary" is simplified and the "Association Indexes" table refers to word co-occurrences only;
- when exiting from this analysis, the dictionary of Thematic Nuclei (i.e. the list of labels assigned to each word cluster) can be exported and, after a thorough revision, can be imported by means of the Dictionary Building function. In this way the user will be able to perform certain second order analyses (i.e. analysis concerning "themes" or "concepts").