Sentiment detection
Overview
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.
Info
This detector should not be confused with the sentiment analysis capability of document analysis, although the two have some similarities.
Categorization
Categorization works in a similar way to document classification and is based on a taxonomy.
Warning
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.
Extraction
Introduction
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.
SENTIMENT_SCORE
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.
SENTIMENT_POS_INFO
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. |
SENTIMENT_NEG_INFO
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. |
ENTITES
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. |
FAITS_DIVERSE
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. |
ECONOMIE_FINANCE
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
- How to request information detection API resources.
- How to interpret the output of the
sentiment
detector.