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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
Texts and Discourses as Dynamic Systems
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
Classification of New Documents
Key Contexts of Thematic Words
Export Custom Tables
Editor
Import-Export Identifiers list
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

Classification of New Documents


This tool, which is very easy to use, allows one to easily classify new documents according to a pre-existing model (i.e. any categorical variable) and also to compare any new document with all documents included in a corpus already analysed.

To this purpose, the following steps are required:
- enter a new document in the appropriate box;
- select a categorical variable to be used as a 'model';
- choose the desired 'objective' and a 'method';
- click 'execute'

All results can be exported by using the right click options (see the below pictures).

When using this tool for sentiment analysis purpose, your corpus must include an appropriate categorical variable (see the below below).

N.B.: When the user wishes to classify a dataset of new documents by using a supervised method, the dataset must be imported by T-LAB and then analysed by using a previously generated dictionary. To this purpose, the Thematic Document Classification can be used, both for generating a dictionary of categories (i.e. unsupervised method) and for performing a supervised classification.