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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

Sequence and Network Analysis


This T-LAB tool, which takes into account the positions of the various lexical units relative to each other, allows us to represent and explore any text as a network.

Various options are available which can be used both for performing a Co-Word Analysis and a Thematic Analysis, as well as Disambiguation tasks.

In fact, after building two matrices in which all pairs of predecessors and successors are recorded, T-LAB calculates the transition probabilities (markov chains) and provides various outputs concerning the target words.
Moreover, it is possible to perform a cluster analysis of the network data and explore the semantic relationships between words either within or between the various 'thematic clusters'. To this purpose, the Louvain method for community detection is used (see Blondel V.D., Guillame J.-L , Lambiotte R., Lefebre E., 2008; ; N.B.: In T-LAB, the analised network consists of directed and weighted links).

That means that the user is allowed to check the relationships between the 'nodes' (i.e. the key-terms) of the network at different levels: a) in one-to-one connections; b) in the 'ego' network; c) within the 'communities' to which they belong; d) within the entire text network.

ONE-TO-ONE
EGO-NETWORK
COMMUNITIES
ENTIRE NETWORK

 

The information concerning how to use the above options is organized in three sections:

A - Exploring one-to-one connections and 'ego' networks;
B - Exploring 'communities' (i.e. thematic clusters) and the entire network;
C - Some technical details.

A - EXPLORING ONE-TO-ONE CONNECTIONS AND 'EGO' NETWORKS

When the automatic analysis is over, several graphs and tables are available which allow us to ckeck the relationships and the data concerning target words (just click any item in the tables or any point on the graphs).

All graphs can be customized and exported in different formats (right click to show pop-up menu).

In two of graphs the items that are closer to the selected one are those that have the higher probability of coming before (predecessors) and after (successors).

PREDECESSORS
SUCCESSORS

In the other cases, the closeness between key-terms is represented by means of the arrow tickness (see below).

All data can be checked by means of various tables.

The INTERACTIVE TABLES show the sorted list of predecessors and successors of each selected item.

The list is in descending order according to the probability values ("PROB"). For example, in the following table, the probability that "camp" will follow "refugee" is equal to 0.067, that is 6.7%.


The option TRIADS (see below) allows us to visualize some tables with sequences of three elements in which the selected item is in the first, in the second or in the third position. For each triad T-LAB shows the corresponding occurrence values. (N.B.: Within the triads the empty words are not included).

The ALL LINKS table (see below), which is particularly useful for word-sense disambiguation, contains all word pairs (i.e. predecessor and successor), as well as their occurrence values. Moreover, by clicking any row of this table, all text segments (i.e. elementary contexts) where the two members of each pair are present at same time (i.e. co-occurrences) will be displayed in HTML format on the right side of the form.

The RANK OF APPEARANCE table, with the frequency and the average order of appearance (or evocation) of each term within the text segments, is only provided when the corpus consists of short texts, such as responses to open-ended questions.

Anytime, by clicking the GRAPH MAKER option, the user is allowed to obtain various types of graphs by using customized lists of key words (see below)
N.B.: Experienced users who are interested in exporting files in different formats (e.g., dl .gml .vna .graphml) with data concerning ALL links, may click the 'SELECT ALL ITEMS' button.

At any point, by right clicking any item on the far left table, the user is able to check the respective concordances (see the picture below).

B - EXPLORING THE THEMATIC CLUSTERS AND THE ENTIRE NETWORK

When performing a cluster analysis, further graphs and tables become available, which allow the easy exploration of all levels of the network hierarchy (see the items marked with the blue rectangles in the below picture).

A first table summarizes the characteristics (i.e. key-terms) of the FINAL PARTITION obtained by the clustering algorithm.
In such a table, the characteristics of each thematic cluster are sorted by the TF-IDF value (see below).
N.B.: When a cluster of the final partition consists of only two words, usually that means a multiword case has not been resolved during the pre-processing phase.


By clicking any word in the above table (as well as in the ALL PARTITIONS table), a TreeMap allows us to check the communities to which it results to belongs (see below).

The MDS MAP and the PERCENTAGES charts (see below) allow us to check the weight of each cluster as well as their relationships within the final partition (see below).

Depending on the number of key-words, two graphs in HTML format allow us to check the relationships between them, either within the entire network or within the cluster they belong to (see below).

RADIAL DENDROGRAM
NETWORK GRAPH (FORCE-DIRECTED GRAPH)

Three other tables provide us with further outputs of the cluster analysis.

In detail:

The ALL PARTITIONS table allows us to check how the key-words have been grouped at each cluster partition (see the below table, in which the numbers in the partition columns refer to the various clusters).
N.B.: In such a table, which - by default - is ordered on the first partition, each shift from one small cluster to the other is marked by highlighting in green the first word which belongs to it.

The INTERMEDIATE PARTITIONS table allows us to check how the key-words have been grouped at any selected cluster partition.
In such a table, the characteristics of each thematic cluster are sorted by their occurrence value (see below).


The TYPICAL CONTEXTS table allows us to check the text segments which have the highest score of association with the clusters of the final partition. In such tables the 'score' refers to the similarity (cosine index) between the feature vector of each cluster and the vector in which each text segment is represented.
N.B. In this table, the most significant text segment of each cluster is highlighted in yellow.

Like other cases of thematic analysis, T-LAB allows us to export the dictionary of the final partition which can be used for further analyses.

C - SOME TECHNICAL DETAILS

The types of sequences that this tool allows us to analyse are the following:

1- Sequences of Key-Words, the items of which are lexical units (i.e. words or lemmas) present in the the corpus or in a subset of it. In this case the maximum number of nodes (i.e. 'types' of lexical units) is 5,000;
N.B.: When the automatic lemmatization is applied, this limit corresponds to about 12,000 words (i.e. raw forms).

2- Sequences of Themes, the items of which are context units (i.e. elementary contexts) tagged by a T-LAB tool for thematic analysis.
N.B.: Since the sequence of elementary contexts (sentences or paragraphs) characterises the entire 'chain' (predecessors and successors) of the corpus, in this case
T-LAB performs a specific form of Discourse Analysis the nodes of which (i.e. 'themes') can vary from 5 to 50.

3 - Sequences recorded in a Sequence.dat file made by the user (see the the explanation at the end of this section). In this case the maximum number of records is 50,000 and the number of 'types' (i.e. nodes) must not exceed 5,000.

The following information is provided to help the user to better understand the data reported in the SUMMARY table.

According to the graph theory, the predecessors and the successors of each node (in this case, lexical unit or theme) can be represented by means of arrows (arcs) coming to (in-degree = types of predecessors) or going out (out-degree = types of successors).

As an example, in the following table "people" has 412 types of successors and 449 types of predecessors.
And its centrality degree is 0.243.

According to their ratio (successors/predecessors), it is possible to verify the semantic variety engendered by each node:


- if the ratio is greater than 1, the node is defined "source";
- if the ratio is equal to 1, the node is defined "relay";
- if the ratio is lower than 1, the node is defined "well".

In the same table, for each lexical unit, the column "cover" (coverage) indicates the percentage of its occurrences preceded or followed by lexical units included in the user list.

When the analysed units cover the totality of those present within the corpus, the cover value is equal to 1; otherwise, it is a lower value.
Moreover: when the cover value is equal to 1, the summations of the probability values (both of predecessors and of successors) are also equal to 1; otherwise, they have lower values.
In both cases, the residual percentage is determined by the fact that there are predecessors and successors not included in the analysis.


For example, the sequence represented in the following image is constituted by 39 events: of these, only 16 (the hypothetical units in analysis) are "covered" (gray boxes). That is because some of them, e.g. those corresponding to the occurrences of the lexical unit "A", have predecessors and successors not included in the analysis (white boxes).

Differently, when the user sequences of themes or sequences recorded in external files all the events are covered.

N.B.: In order to analyse an external file, the user must prepare a 'Sequence.dat' file; then, after opening an existing project, he must select the 'Sequences recorded in a Sequence.dat file' option..

The calculation method, the graphs and the tables are analogous to those already described (see above).

The Sequence.dat file, which can contain numerous kinds of tags (e.g. names of speakers in a conversation, categories obtained by content analysis, kinds of events, etc.), must be made up by "N" lines (min 50 max 50,000), each with a tag of a max of 50 characters, without punctuation marks or blank spaces.

Tag types must be max 5,000.

Here are some lines of Sequence.dat files in the correct format:

Hamlet
King
Hamlet
Queen
Hamlet
Queen
Hamlet
King
Queen
Hamlet
King
Hamlet
Horatio
Hamlet
Horatio
... ... ...


activist
food
genetic
conservative
activist
genetic
conservative
activist
commerce
conservative
activist
conservative
biology
society
activist
... ... ...


event_01
event_03
event_02
event_03
event_03
event_01
event_05
event_02
event_05
event_01
event_02
event_04
event_03
event_01
event_01
... ... ...

Both in the case of sequences concerning the corpus lexical units (or themes) and of those included in an external file (Sequence.dat), T-LAB produces several working tables which can be found in the MY-OUTPUT folder.