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Make sense of large amounts of data

Content analysis

Analyze the text, extract keywords and concepts, identify topics, infer relationships, understand the mood and opinions. 

The problem

There is a vast amount of data that can be used to support an organization's decisions. This data can be internal, from years of data collection, or external, produced by users, public organizations and other businesses. Mannually sorting it or trying to understand it is no longer an option. Thanks to machine learning techniques we are able to extract information, pinpoint to the important topics and concepts.

All Devices

Our Tools

Keyword extractor

Given a set of texts, extract keywords, keyphrases and features mentioned in every text.
Unsupervised | Language agnostic | Multi-token keyword extraction

Entity extractor

Define a set of Named Entity tags (Person, Organization, Location, GPE, diseases, etc.), detect and highlight the entities mentioned in the text.
Across many languages and domains (News, Twitter, Biomedical, etc.)

Sentiment analysis

Classify the entire text as negative, positive, or neutral and explain how the classifier decided (visualizing attention)

Across many languages and domains (Reviews, Twitter, Campaings, etc.)

Entity based sentiment analysis

Detect the polarity of the text towards an entity (which may be part of the text or not) and explain how the classifier decided.

Across many languages and domains (Reviews, Twitter, ChatBots, etc.)

Topic modeling

Analyze large document collections, then process and sort this information by applying probabilistic models to extract hidden topics. Discover the main topics talked about.

Unsupervised | Sector specific | Language specific

Some of our solutions

Check some of our solutions (non-exhaustive) we have developed and used in various settings. 

  • Innovation Extraction

    Given a text and an innovation taxonomy (optional), segment the text into sections and classify every sentence – if it conveys an innovation statement and what type of innovation

    Domain agnostic (Biomedical, Computer Science, etc.)
  • Identify topics for rare diseases

    Analyse scientific publications and policy documents in the health domain and identify topics in rare diseases.

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