Unlock the meaning in your French data.

Our AI-driven French sentiment API determines the sentiment in your French text. All analysis is done natively in French, no translations are used leading to greater accuracy.

Your French Sentiment Analysis Company

The Challenge

Not all languages are the same; grammar rules vary from one language to another and the rules of verb conjugation, noun-verb agreement, and negations vary from one language to another. This can make it extremely difficult to gauge text analytics without the proper tools in place to understand the nuances of other languages.

Should translations be used for French sentiment analysis?

French is a unique language and it differs from English in a number of ways: from sentence structure to words and phrases that may be used differently, using the same techniques and language models that work for English sentiment analysis when conducting French sentiment analysis would yield terribly inaccurate results.

Repustate Sentiment Analysis

Because of the challenges for applying high-level sentiment analysis for French companies, Repustate has developed French-specific tools to decipher words, industry jargon, and the feelings behind your customer's words. French sentiment analysis, including an French part of speech tagger, an French lemmatizer, and of course, French-specific sentiment models.

What are the basic steps in French sentiment analysis?

French part-of-speech tagging allows us to narrow in on where the sentiments may lie within a block of text. Verbs, nouns, and adjectives provide the cues necessary to determine sentiment and aid in detailed analysis. In order to create a fast and accurate French part-of-speech tagger, data scientists have to have a massive corpus of manually tagged French text. This French text can then be fed into a machine-learning algorithm to create an French part-of-speech tagger.

The larger the corpus, and more importantly, the more varied the corpus, the better the results in creating the French part-of-speech tagger. Repustate has created a massive corpus of French text grabbing data from a variety of sources to ensure good coverage.

Taking it a step further, by using French Named Entity Recognition with the advanced semantic search for enterprises, we can identify brand and business entities in data. No matter how misspelled a word is, our API will reproduce the first name in native script and thus improve the accuracy of name searching, transliteration, and the grade of identity verification initiatives. This gives high accuracy ranked results, on the basis of the linguistic, phonetic, and specific cultural variation patterns of the names

French language sentiment models

Repustate has developed sentiment language models specific to French to capture the various phrases, idioms, and expressions that help define sentiment when writing in French. Understanding the various grammatical aspects of the French language that make it unique and very different from English is what allows Repustate's French sentiment analysis to be as fast and as accurate as it is.

Applications of Text-based Sentiment Analysis in French

Our French semantic analysis API is optimized to understand and analyze your data in standard French language and dialects. Our API can be used to analyze the public sentiment about a product, service, government policy opinions, stock market trends, and social media listening. Let`s take a look at a few applications:

A Lyon based healthcare corporation wants to understand the doctor-patient relationship to make the whole system more sensitive towards healthcare, Repustate’s French sentiment analysis can enable them to semantically identify various aspects related to doctor-patient interaction and sentiment related to those aspects. With Repustate’s sentiment and text analytics, this Lyon based healthcare system can have the answers they’ve been searching for in half the time it would have taken before.

To take a step further, let's take an example of a Paris based finance corporation that deals in the forex market and monitors on International market news to tap into market trends. Repustate’s forex sentiment analysis unleashes a whole new arena for growth opportunities for the corporation by projecting and alerting them with semantically driven trends in the market.

Finally, let’s take a look at how the Marseille based Government agency benefits from sentiment analysis. As a public transport agency, it would like to understand the sentiment of daily commuters using public modes of transportation. Repustate’s sentiment analysis can find the hidden sentiment through social media channels and social forums and understand user opinion about the service to decide what they need to update in their system to have happy customers.

French Enterprise Search

Repustate's Enterprise Search automatically annotates your French data with semantic information. This includes relevant entities, topics, and entity-specific metadata. Search any and all metadata associated with any given entity that Repustate finds. You can search by market cap or industry type for business, or perform your search by nationality. Over 100 metadata properties can be searched, and all of them can be automatically determined by Repustate’s French Enterprise Search.

Toutes les langues ne sont pas les mêmes

Les règles de grammaire varient d'une langue à l'autre. Les règles de conjugaison des verbes, l'accord nom-verbe et les négations varient d'une langue à l'autre.

Le français est une langue unique et elle diffère de l'anglais de plusieurs façons. Utiliser les mêmes techniques et les mêmes modèles de langue qui fonctionnent pour l'analyse des sentiments de l'anglais lors qu'on effectue l'analyse des sentiments de français donnerait des résultats horriblement imprécis.

Voilà pourquoi Repustate a conçu des outils propress à français pour aider à l'analyse des sentiments de français, y compris un étiqueteur morpho-syntaxique de français, un lemmatiseur de français et, bien sûr, des modèles de sentiments propres à français.

Étiquetage morpho-syntaxique de français

L'étiquetage morpho-syntaxique de français permet à Repustate de cerner l'endroit où les sentiments peuvent se situer dans un bloc de texte. Verbes, noms et adjectifs fournissent les indices nécessaires pour déterminer les sentiments.

Afin de créer un étiquetage morpho-syntaxique rapide et précis de français, vous devez avoir un corpus massif de texte de français manuellement étiqueté. Ce texte de français peut ensuite être introduit dans un algorithme d'apprentissage automatique pour créer un étiqueteur morpho-syntaxique de français.

Plus le corpus est grand, et plus important encore, plus le corpus est varié, plus les résultats dans la création de l'étiqueteur morpho-syntaxique de français. Repustate a créé un corpus massif de texte de français saisissant des données de diverses sources pour assurer une bonne couverture.

Les modèles de sentiments de langue de français

Repustate a conçu des modèles de sentiments de langue propres à français pour saisir les diverses syntagmes, locutions et expressions qui aident à définir les sentiments en écrivant en français. Comprendre les différents aspects grammaticaux de la langue français qui la rendent unique et très différente de l'anglais est ce qui permet à l'analyse des sentiments de français de Repustate d'être aussi rapide et précise qu'elle est.