Unlock the meaning in your Dutch data.

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

Your Dutch 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 Dutch sentiment analysis?

Dutch 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 Dutch sentiment analysis would yield terribly inaccurate results.

Repustate Sentiment Analysis

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

What are the basic steps in Dutch sentiment analysis?

Dutch 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 Dutch part-of-speech tagger, data scientists have to have a massive corpus of manually tagged Dutch text. This Dutch text can then be fed into a machine-learning algorithm to create an Dutch part-of-speech tagger.

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

Taking it a step further, by using Dutch 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

Dutch language sentiment models

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

Applications of Text-based Sentiment Analysis in Dutch

Our Dutch semantic analysis API is optimized to understand and analyze your data in standard Dutch 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 Amsterdam based healthcare corporation wants to understand the doctor-patient relationship to make the whole system more sensitive towards healthcare, Repustate’s Dutch 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 Amsterdam 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 Hague 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 Rotterdam 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.

Dutch Enterprise Search

Repustate's Enterprise Search automatically annotates your Dutch 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 Dutch Enterprise Search.

Niet alle talen zijn hetzelfde

Grammatica regels verschillen per taal. De regels van werkwoordspelling, het verband tussen zelfstandig naamwoord en werkwoord, en tegenstellingen zijn in elke taal anders.

Nederlands is een unieke taal en het verschilt in enkele opzichten van Engels. De zelfde technieken en taalmodellen gebruiken, die voor Engelse sentimentanalyse werken, bij een Nederlandse sentimentanalyse, zou verschrikkelijk inaccurate resultaten opleveren.

Daar heeft Respustate Nederlands-specifieke hulpmiddelen ontwikkeld, om te helpen bij Nederlandse sentimentanalyse, inclusief een Nederlandse part-of-speech tagger, een Nederlandse lemmatisator en natuurlijk Nederlands-specifieke sentiment modellen.

Nederlandse part-of-speech tagging

Nederlandse part-of-speech tagging helpt Respustate te ontdekken waar sentiment kan liggen in een zeker stuk tekst. Werkwoorden zelfstandige naamwoorden en bijvoeglijke naamwoorden vormen de hints die nodig zijn om sentiment te ontdekken.

Om een precieze Nederlandse part-of-speech tagger te maken, moet je grote hoeveelheid aan handmatig gelabeld Nederlandse teksten hebben. Deze Nederlandse teksten kunnen dan in een machine worden ingevoerd, die met behulp van een leer algoritme een Nederlandse part-of-speech tagger maakt.

Hoe groter de tekst, en belangrijker, hoe gevarieerder de tekst, hoe beter de resultaten van de Nederlandse part-of-speech tagger. Respustate heeft een enorme aantal Nederlandse teksten van verschillende bronnen om zoveel mogelijk data te hebben voor een zo goed mogelijk resultaat.

Nederlandse taalsentimentmodellen

Respustate heeft taalsentimentmodellen ontwikkeld, speciaal voor Nederlands, om verschillende frasen, idioom en expressies te omvatten, die helpen bij het definiëren van sentiment bij het schrijven in het Nederlands. Het begrijpen van de verschillende grammaticale aspecten van de Nederlandse taal, die het uniek en zeer verschillend van Engels maken, is wat de Nederlandse sentimentanalyse van Respustate zo snel en nauwkeurig maakt als het is.