Content analysis
The problem
There is a vast amount of data that can be used to support decisions within an organisation. This data can be internal, from years of data collection, or external, produced by users, public organisations 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.
Keyword extractor
Entity extractor
Define a set of Named Entity tags (Person, Organisation, Location, GPE, diseases, etc.), detect and highlight the entities mentioned in the text.
Sentiment analysis
Classify the entire text as negative, positive, or neutral and explain how the classifier decided (visualising attention)
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.
Topic modeling
Analyse large document collections, then process and sort this information by applying probabilistic models to extract hidden topics. Discover the main topics talked about.
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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
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Identify topics for rare diseases
Analyse scientific publications and policy documents in the health domain and identify topics in rare diseases.
