Repustate's sentiment analysis API is powered by Dutch natural language processing (NLP). It analyzes Dutch data natively, without the need to translate any document into English, thus, giving more accurate and meticulously useful insights.
Repustate's Dutch sentiment analysis solution is made dedicatedly for the Dutch-speaking world. The tool can be used to analyze data from any Dutch source such as news, social media, blogs, reviews, and surveys.
The solution has its own dedicated Dutch part-of-speech tagger, Dutch lemmatizer, and Dutch-specific sentiment models. It also understands colloquial words and industry jargon. Dutch natural language processing, coupled with named entity recognition (NER), helps it identify topics and themes in the data for more granular sentiment analysis, leading to key business insights.
See Repustate's Dutch sentiment analysis in action
What are the benefits of Repustate's Dutch Sentiment Analytics Tool?
The Repustate Dutch Sentiment Analysis solution is made specifically for Dutch data. It is highly
customizable and can be calibrated to be an exact fit for your business. Listed below are the main advantages of the tool:
Obtain granular Dutch sentiment analysis by aspect
Data security with on-premise deployment or through Cloud
Custom-made model that can be specific to your brand and industry domain
Multilingual sentiment analysis capability to help you scale globally fast
Why Choose Repustate Over Others?
The Repustate model has the highest accuracy in NER compared to other
tools. It is not an off-the-shelf product, but a highly personalized,
scalable solution with dedicated customer support. Here is why our clients worldwide (NA, EMEA and Asia Pacific) collaborate with us.
Premium Support: As our client, you will have a dedicated engineer assigned to your account.
Technology: We apply artificial intelligence and machine learning to Dutch natural language processing for the best results.
Dutch Text: We never translate. Repustate has unique speech taggers and sentiment models made just for Dutch text analysis. In fact, each of our 23 languages has an ML model based on the natural language natively.
Accuracy: Our aspect-driven approach to sentiment is granular, accurate, and targeted.
Speed: Our API can process 1,000 comments per second.
Customizable: We custom-build the model to capture your most important aspects and topics.
Scale: Our API can easily scale from 1 to 10 million to 100 million documents and beyond.
Integration: The Repustate solution integrates seamlessly with your existing technology. You do not need 3rd party support for any underlying technology.
Competition: Our model is the most accurate in NER compared to the competition that includes Google, Amazon, and Microsoft.
Check out our comparison.
Deployment Flexibility: We offer both, a Cloud model, and an on-premise installation for data security, in a one-click solution.
No Hidden Fees: Our pricing includes support, updates, training, and deployment.
Iterative: Our models get more and more intelligent with training and as they process more data.
Dutch Language Sentiment Models
Repustate has developed sentiment language models specific to Dutch
to capture the various phrases, idioms, and expressions that help define certain sentiments. It turns unstructured
Dutch data into business intelligence you can use to increase your value proposition, brand experience, and value delivery.
Take a quick tour of Repustate's Dutch text analytics solution
What are the Basic Steps in Dutch Sentiment Analysis?
Repustate has a massive corpus of Dutch text that has been tagged manually,
as the first step towards Dutch sentiment analysis. This corpus is then processed
by an ML model for high precision in aggregate sentiment scoring. The steps can be laid out as:
Collect a massive, highly varied corpus (collection of texts) of the manually tagged Dutch text.
Feed this text into an ML-based algorithm to create an Dutch part-of-speech tagger.
Granulate the algorithm for deeper accuracy using NER.
Extract the sentiment score for each aspect, theme, and topic.
Repustate uses Dutch Named Entity Recognition (NER) with
advanced semantic search to identify brand and business entities in data. No matter how misspelled a word is,
the Dutch NLP model will reproduce the name in the native script and give accurate name search,
transliteration, and identity verification measures. This gives high-accuracy ranked results, based on the
linguistic, phonetic, and specific cultural variation patterns of the names.
Should translations be used for Dutch sentiment analysis?
Trying to achieve Dutch sentiment analysis by using an API that uses translations can
yield incorrect results. So, the answer is, no. As your Dutch sentiment analysis company,
Repustate, uses intricate Dutch NLP for higher accuracy in sentiment scoring of your data.
This is because translations dilute the nuance of a statement. If a text has multiple topics,
sentiments, and themes, as reviews and comments usually do, the aggregate semantic score
of the data (+1 for positive, and -1 for negative) will be inaccurate with translations.
Applications of Dutch NLP in Sentiment Analysis Tools
Repustate's Dutch sentiment analysis API helps you get useful insights through
Social media listening
from Facebook, Twitter, Instagram, and even video-based platforms like TikTok and YouTube.
This is very useful to brands who want to capture sentiments around specific facets
of their business, product line, or service.
Analyze news - text, audio & video:
Sentiment analysis from news streams is really easy with Repustate's Sentiment Analysis API.
Whether you want Dutch NLP analysis for employee surveys or product reviews, our tool gives
you relevant insights. Furthermore, its visualization dashboard converts the data into charts,
graphs, and tables, so you can understand the data easily.
Analyze surveys, forums, and Google reviews:
Companies dedicate a large number of resources to understanding their customers by
running feedback campaigns in their stores, social forums, mobile apps, and websites.
Repustate's Dutch Voice of Customer analysis
helps you make sense of all that data.
Understand public sentiment:
From policy decisions to bad customer service, whatever the issue may
be, people share this information with the world. This data can be vital to
a government body, or a company looking to improve its brand perception and
Let Repustate's Dutch sentiment analysis uncover your hidden insights
Real-World Applications of Dutch Text Sentiment Analysis
Repustates's Dutch sentiment analysis API is made for the Dutch language and its dialects.
Powered by Dutch NLP, the solution gives you accurate and fast insights through
aspect based sentiment analysis
of your data. Here are some real-world examples of how Repustate has provided Dutch
sentiment analysis to organizations in various industries.
When the prestigious
Nahdi Medical Company,
based in Saudi Arabia, approached Repustate, its biggest motivation was the need to understand
patients better. They wanted to overhaul the system and deliver a better patient experience.
They remained dissatisfied with the speed and accuracy of other tools in the market as they
could not grasp the complexity of Dutch. Repustate's sentiment analysis API was at last
able to help the client meet its objectives. Our Dutch NLP-powered solution gathered
impactful insights more accurately and faster than others. It semantically identified various
aspects related to patient care and provided the client with key revelations.
A Hague-based financial corporation realizes that it's not good for business if they
are unable to monitor global financial and stock market news accurately and fast. Since even
names of companies change when the news is in a foreign language, the company realizes it is
missing out on vital information in an industry that thrives on quick decision-making.
Repustate's Dutch sentiment analysis tool provides them with a highly-precise,
customized, stock sentiment analysis solution powered by Dutch NLP.
Our sentiment analysis solution gives them insights into
market sentiment based on price movements of securities traded, as well as
financial news coverage in all major languages. They also leverage the
real-time dashboard that shows market sentiment scores and share prices for
different debt instruments and equities in an easy-to-understand format.
A public transport agency from Rotterdam wants to improve its service and brand perception
and approach Repustate with the problem. They want to provide better service by addressing
negative feedback and restructuring resources. They want to have an authentic and detailed
understanding of the sentiment of daily commuters using public modes of transport.
Repustate's Dutch sentiment analysis API helps them understand their data. By applying
Dutch natural language processing techniques to all the reviews and comments gathered
from public forums, Repustate's solution is able to give them useful insights they can use
to achieve their goals.
Dutch Enterprise Search
Repustate's Enterprise Search automatically annotates
your Dutch data with semantic information. This includes relevant entities, topics, and
entity-specific metadata. You can search all metadata associated with any given entity that
Repustate finds 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.
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.