Your Text Analytics Project

Our Three Phase Approach

You have a new

Business Idea

 

$

We show you how to realize it

Our onboarding workshops quickly assesses if the problem can be solved with text analytics, and how much resources will be needed.

 

  • Duration: 2 hours
  • Cost: CHF 600.00 plus taxes

You want to

Test your Idea

$

We build a running prototype

We develop a first prototype on your data, evaluate the resulting quality and give a recommendation for implementing a productive solution. This usually takes 5-15 days and shows how good the results will be.

 

  • Duration: 5-15 days
  • Cost: Fixed

You want to

Go Live

$

We develop a software solution

We implement a software component that solves your text analytics task. This includes definition of architecture and interfaces (e.g. REST), performance optimization (quality and runtime) and deployment in your infrastructure. Time and cost depend on the size of the project.

 

  • Duration: flexible
  • Cost: project-dependent

We are multilingual

We can easily apply our technology to any language. Usually we just need a good set of training examples in the new language. If no such training data exists, we can help you generate it.
We already implemented solutions in many languages, including German, Swiss German, French,  Italian and Arabic.

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Sample Solutions

Topic Categorization for Swiss News Articles

The Swiss Economic Archive has gathered more than 2.5 million news documents on Swiss economics, politics, and companies since 1910. We developed an automatic topic categorization for approximately 600 pre-defined topics.

Generation of Company Reports

Our partner developed an online platform for an Indian professional service provider with 3000+ employees. We implemented machine learning algorithms that support automatic generation of company reports by finding static company data (e.g. founding year), identifying competitors, and generating company descriptions from online news.

Real-time Integration of Social Media

We automatically matched entries from a customer database with Facebook fans, enriched the data with sentiment analysis on the posts, and integrated the results into the CRM.

German Sentiment Corpus

We manually annotated 10’000 German and 3’000 Swiss-German Texts with sentiment labels (positive, negative, neutral). The corpora are publicly available

Statistics of Job Offerings in Switzerland

For the annual report of JobCloud, we analyzed sentiment and word statistics for more than 140’000 job offerings.

Sentiment Analysis

Our sentiment analysis system uses deep learning to detect the tonality of a text (positive, negative, or neutral). It won two international competitions, SemEval-2016 and Evalita-2016.

Health Risk Prediction from Twitter

We gathered 150 million tweets and correlated public health data on heart diseases and diabetes with frequent twitter topics. Our model can predict mortality rates based only on the tweets of users.

Chatbot for Movie Recommendations

We implemented a chatbot for movie recommendations, which communicates with the user in natural language and selects the most appropriate movies based on the users preferences.

Text analytics

The largest set of data we

worked with contained over

 

17’000’000’000

 

documents

Frequently Asked Questions (FAQ)

Do you have your own software products?

YES. We have developed solutions for many classical text analytics tasks, such as sentiment analysis, named entity recognition, language detection, topic modeling etc. They are state-of-the-art, as we have shown in several international competitions.
We can use our solutions to realize your project, but we also integrate third-party services and libraries, e.g. by Microsoft, IBM or other service providers.

Can I get the source code?

YES.  We usually hand-over the complete source code to our clients. You get all rights to modify and re-use it. This allows you, for instance, to re-train the machine learning models on your own if your data has changed at some time.

How can we integrate your solution in our infrastructure?

We always aim to deliver a software component with a very simple interface. For instance, our sentiment solution takes a plain text (e.g. a tweet) as input and outputs its sentiment (positive, negative, neutral) and some meta-data in a simple JSON format.
We usually use Docker to wrap our solutions, which makes it easy for you to deploy and scale it on your infrastructure.

Do you also do images, audio or other data types?

Our primary focus is on text analytics, where we are mostly working on. But our team has also experience in other domains, e.g. speaker detection, image classification or predictive analysis.
In addition, we have a huge network of partners in both academia and industry, where we can usually find a suitable expert.

How good will the results be?

There are three aspects to consider:

  1. In general, the more time we invest, the better gets the solution. Every text analytics algorithms has several parameters that we can optimize, and tuning every step in the process usually improves the results.
  2. Every problem has an intrinsic complexity. For instance, the best solutions for sentiment analysis have an accuracy of approx. 73% (on tweets, 3 classes positive, negative, neutral), whereas named entity extraction can be solved with up to 93%.
  3. Your underlying data is important. Many tasks in text analytics are solved with machine learning, and these algorithms are trained and optimized on your data. The more data they have for training, the better the results.

Data Security

  • On premise data analysis: none of your data leaves your house
  • Swiss Made Software
  • Respect for data protection and privacy

We build technologies for other types of data as well!

Contact Us
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