Angoss Text Analytics uniquely allows users to merge the output of unstructured, text-based analytics with structured, proprietary data to perform data mining and predictive analytics with additional predictive variables for improved accuracy—and greater insights.
Today, 80% of business relevant information originates in unstructured, primarily text, form. This valuable data may be buried in social media (blogs, tweets, forum posts and newsfeeds) and other voice of the customer (VoC) channels such as call center logs, emails and other forms of communications.
Angoss Text Analytics provides entity and theme extraction, topic categorization and sentiment analysis. Angoss has partnered with Lexalytics®, the leading text and sentiment analysis engine provider, to seamlessly embed the Salience Engine into the Angoss predictive analytics software suite.
Angoss Text Analytics can be deployed either on-premise or on-demand in the Cloud for small teams, short-term or project based initiatives. It is available for use on Windows and Red Hat Linux platforms and includes the following:
| Text Analytics Highlights |
| Text acquisition from databases in the In-database analytics mode via ODBC |
| Entity and theme extraction |
| Topic categorization by Concept Topics, query topics and user defined entities |
| Sentiment analysis |
| Document summarization |
| Entity/theme attraction maps and visualization |
| Data mining view generation with text analysis results |
| Data profiling, segmentation, predictive modeling, cluster and association analysis based on text analysis results |
| Data preparation tools to merge text analysis dataset with structured data (join, append and aggregate, and remove duplicates) |
The ability to mine and analyze unstructured, text-based data and apply predictive techniques allows organizations to discover insights and customer intelligence previously unavailable.