Text Mining: Semantic Text Analysis based on Azure Machine Learning algorithms

Text mining is an automated process of deriving high-quality information from text. Its main difference from other types of data analysis is that the input data is not formalized in any way, which means it cannot be described with a simple mathematical function.

WaveAccess offers a Text Mining solution based on Azure ML algorithms to analyze structured and unstructured text data. The solution can be used for analytical purposes like

• Data search in unstructured arrays
• Text topics extraction
• Facts extraction
• Text semantic (meanings) extraction
  - summarization as extractioning
  - summarization as abstractioning
• Keywords and named entities extraction
• Categorization
• Multiple text metrics evaluation
• Sentiments analysis (Empathy, Dissatisfaction, Satisfaction categories etc)

• Saves time and labor costs by automating manual work
• Ensures services based on analyzing big amount of text data
• Mitigates risks of not receiving crucial information at the right time
• Helps making smart decisions on what is fraud, what is risky and what is relevant for business

• Automatic customer request sorting by type, complexity, priority, or profitability, and further passing them on to agents;
• A knowledge base for the company can be created based on the accumulated text data;
• Quick acquisition of relevant data from databases, to speed up workflows and management;
• Risk management: text mining technology enables complete management of thousands of sources of text documents, and provides the ability to link together information and be able to access the right information at the right time;
• Fraud detection through claims investigation: The majority of information in jurisprudence is collected as text. Insurance companies use text mining technologies by combining the results of text analysis with structured data to prevent frauds and swiftly process claims
• Spam and unwanted content filtering.Spam impacts business productivity and safety due to viruses. Text mining techniques can be implemented to improve the effectiveness of statistical-based filtering methods.