Text Mining Software

The purpose of both data mining software and text mining software is to distill meaning from a large mass of sources. These advanced systems automatically and minutely examine large bodies of data, and extract information or patterns that are not readily apparent to the human eye.

While data mining software analyzes structured data, text mining software extracts pre-defined types of information from unstructured textual sources. One example of the use of this software is sentiment analysis – seeking out evidence of sentiment expressed in large amounts of text, quantifying and qualifying the sentiment, and providing detailed end user reporting.

Traditionally, text analytics software systems have used three main approaches to extract meaning:

• Term extraction – in which all terms are extracted from a given document and a histogram is created indicating the frequency of appearance of all terms.
• Text classification or categorization – in which a given document is classified by the text analytics software according to a set of pre-defined categories.
• Information extraction – the most advanced approach, which uses an intelligent text analysis system to extract entities and events from a given document, and create a structured database from this information.

For what is Text Mining Software Used?

Data mining software has been in use for many years, and has been applied to numerous and diverse areas in corporate, government, and scientific scenarios.

Text mining software has been mainstream for around a decade only, and has been commercially applied to many fields including product reviews and brand management. Recently, this unique branch of data mining software has been applied to the stock market advice arena, specifically to the creation of sentiment analysis tools that can help identify market trends.

Text Mining Software for Stock Market Advice

Sentiment analysis, as defined above, is a powerful tool when applied to the dynamic and complex stock market. As any modern investor knows, sentiment - as measured through feeling expressed in written media - is a critical force behind the market in the Internet age.

Advanced hybrid text analytics software like The Stock Sonar use an optimal combination of the above-mentioned text mining approaches to deliver higher relevancy, broader breadth of content, and greater accuracy for investors.

By leveraging both supervised machine learning and advanced knowledge engineering, this system delivers an accurate score of sentiment in a given body of text (article, Tweet, blog post, etc.). Text mining software like The Stock Sonar is further able to intelligently recognize and highlight information in context, with an unprecedented level of accuracy.

In such a complex and dynamic environment such as the stock market, the hybrid approach to text mining software is crucial to the precision and reliability of stock picking software. Only through adoption of such sentiment analysis software can investors automatically - yet accurately - distill value from the vast amount of text available, and tap into the true driving force behind the market.