Data Driven Business recently conducted interviews with text analytics professionals from a number of leading companies and identified 10 trends in text analytics that can be observed over the next 6-12 months.

1. Fusion of text (unstructured) data with structured data to achieve results that cannot be achieved by either data set alone. "A good example of this are cell phones that interpret ambiguous sounding names by referencing the structured contacts list". Dave Tomala, Sr. Director of Analytics - Knowledge Solutions, Express Scripts Inc

2. Increase in interest in multilingual text analytics. "Vendors from outside the US are slowly making headway in text analytics for languages other than English, and their clients are our international competitors. Language challenged Americans should take note". Meta Brown, Text Analytics Speaker, Consultant

3. Algorithmic understanding of social media comments. "In recent years, text analytics has made a significant footing in social media analytics impacting brand management and viral marketing promotions". Rahul Saluja, Manager - Web Analytics, Home Depot

4. Commercialization of sentiment detection. "Interest of companies to social media sentiment monitoring is increasing" Han-Sheong Lai, Director of Consulting, Operational Excellence & Customer Advocacy, PayPal

5. Finding trends and trending events in news streams. "Uncovering new trends - historical data is good for helping to predict future things - as long as the environment is consistent. Text analytics help me uncover new trends." Allen Thompson, SVP Corp & Comm Analytics & Reporting, Bank of America

6. More built-in visualization capabilities. "Better visual tools for understanding relationships in text are becoming available" Janine Johnson, Director of Analytics, ISO

7. Streaming real-time text analytics. "Real-time sentiment analysis becoming more and more popular" Nathanael Ford, Senior Analyst - Channel Optimization, United Airlines

8. Use of text analytics for getting insights from unstructured Big Data."Everyone is talking about Big Data. The real issue is how you can extract relationships between textual information and disparate objectives." Michael Skinner, Manager: Patent Analytical Tool Evaluation & Reverse Engineering, Intel

9. Advances in machine learning. "The advent of high-performance machine learning algorithms that enable rapid training over very large, high-dimensional vector spaces". Mark Pitts, Senior Vice President, Analytics, SourceHOV; Former Director, Data Science, Solutions & Strategy, UnitedHealth Group

 10. Integration of different capabilities. "Today we can see the integration of categorization/coding/rule building, predictive modeling and sentiment analysis, visualization." Caio Peixoto, Supervisory Analyst, Information Management, Board of Governors of the Federal Reserve System.

Source: KDNuggets.com