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Sentiment detection


The sentiment detector aims at categorizing documents according the sentiment and emotion analysis.

The detector also also extracts potentially positive or negative terms, facts and events in generic news and financial articles.


This detector should not be confused with the sentiment analysis capability of document analysis, although the two have some similarities.


Categorization works in a similar way to document classification and is based on a taxonomy.


Unlike the API resources dedicated to document classification, this is an information detector and the category tree of its taxonomy is not obtainable with API self-documentation resources like for document classification taxonomies, so it is indicated below.

The detector taxonomy (below) includes a positive and a negative cluster (émotions positives and émotions negatives).

1000 émotions
    1100 émotions négatives
        1110 rage
        1120 inquiétude
        1130 détresse
        1140 contrariété 
        1150 surprise 
    1200 émotions positives 
        1210 joie
        1220 affection
        1230 satisfaction
        1240 sérénité 
        1250 surprise

A surprise (category surprise) can be positive or negative, that's why the corresponding category is included in both clusters.



The information extraction activity of the detector finds and returns records of extracted information. Each record contains data fields and its structure—the possible fields—is called template.
A template can be compared to a table and the template fields to the columns of the table, as shown in the following figure.


The SENTIMENT_SCORE template includes sentiment polarity and score.

Class Description
polarite Sentiment polarity, possible values are positive, negative and neutre.
score Sentiment score.

Polarity and score are derived from categorization scores: the cumulative score of categories under the émotions négatives cluster is subtracted from the cumulative score of categories under the émotions positives cluster. Polarity is neutral (polarite equal to neutre) when the score is between 0 and 2.


The SENTIMENT_POS_INFO template extracts information about the elements of the text that contributed to positive sentiment.

Class Description
facteur_semantique_pos Term with positive connotation.
facteur_declencheur_pos Cause or object positive terms refer to.


The SENTIMENT_NEG_INFO template extracts information about the elements of the text that contributed to negative sentiment.

Class Description
facteur_semantique_neg Term with negative connotation.
facteur_declencheur_neg Cause or object negative terms refer to.


The ENTITIES template extracts references to named entities mentioned in the text, for example:

Les viennoiseries du Café de Flore sont une tuerie.

Class Description
entite Named entity.


The FAITS_DIVERSE template extracts facts and events with a potential positive or negative connotation (for example: crise sanitaire).

Class Description
potentielement_negatif Potentially negative fact or event.
potentielement_positif Potentially positive fact or event.


The ECONOMIE_FINANCE template extracts financial or economic terms with a potential positive or negative connotation (for example: reprise économique or récession).

Class Description
potentielement_negatif Potentially negative financial or economic term.
potentielement_positif Potentially positive financial or economic term.

Useful resources