Sophia Analytics is an advanced and enduring solution to extracting value from linguistic data and obtaining actionable results. The purpose of Text Mining is to process unstructured (textual) information, extract meaningful numeric indices from the text, and, thus, make the information contained in the text accessible to the various data mining (statistical and machine learning) algorithms.SSE is able to read and analyze any type of text, identify quickly and precisely the relationship, associations and trends, allowing data scientists, researchers, business analysts and managers to concentrate on what they do best: giving data a meaning.
Through an integrated Text Mining and Analytics Workbench, Sophia Semantic Engine allows the user to organise and classify contents. We can quickly configure the engine for the specific area of interest. The user can supervise the process and confront the results with other available data. The results can be given directly to the business in the form of reports, or through a web interface, i.e. a real time collaborative dashboard, or feed other applications through API.
Sophia Semantic Engine employs the most advanced semantic and linguistic technologies to give immediate insights and understandings from the get go. SSE is scalable and can be integrated in complex enterprise environments. It can therefore support managers in their decision-making processes in a variety of areas: customer experience, marketing, customer operations, CRM, IT and Quality.
Sophia Analytics allows you to:
- Give meaning to unstructured data
- Transform language into concrete data
- Create expertise and value
Operating model and skills
Sophia Analytics allows an easy management of purchase, as well as exploration and organisation of Big Data obtained through customer interaction:
- Definition of information sources, both inwards (questionnaires, contact center, help desk) and outwards (web and social media)
- Identification of relevant topics, clustering, text mining, semantic annotations, sentiment analysis
- Semi-automatic definition of the classification rules, integration of a machine learning approach with a rule-based approach
Semantic engine allows:
- Understanding relevant topics using machine learning algorithms which reveal the meaning of texts
- Creating classifications and matching automatically sentiment to the topics using semantic rules based on their meaning
- Analysing the results and cross-matching the information by using algorithms which are part of predictive and descriptive analysis