Large-Scale Sentiment Analysis for News and Blogs

Background:

Sentiment analysis of natural language texts is a large and growing field. Some methods for generating sentiment lexicons assume positive and negative sentiment using synonyms and antonyms. Such methods may not accurately capture the sentiment of a word. Other methods use semantics, such as "and" and "but", or tone/orientation to determine a sentiment of a word, but such methods may have low accuracy. Current methods for analyzing sentiment treat only single complete documents, for example, to determine if a movie review is good or bad or quantify opinion from a product review. Therefore, there is a need for a method of generating a more accurate sentiment lexicon and for determining a sentiment over a plurality of texts.

Technology Overview:

This technology uses statistical analysis of text streams to simultaneously monitor changes in reputation to thousands of distinct news entries. Commercial applications of this technology include 1) market research - the technology can analyze the reputation of people, products, and companies without the need for expensive surveys or polling, 2) financial analysis - the conversion of news data to time-series facilitates automated investment analysis, e.g. strengthening pair trading investment strategies by identifying companies without the need for expensive surveys/polling, 2) internet search engines - augmenting results by providing sentiment data on articles.

Advantages:

Gives the ability to monitor entity sentiment as a time-series in any text stream, such as news or blogs, even if written in different languages and from different news sources.

Applications:

Commercial applications include: - Market Research- This technology can analyze the reputation of people, products, and companies without the need for expensive surveys or polling - Financial Analysis- The conversion of news data to time-series facilitates automated investment analysis - Internet Search Engines- augmenting results by providing sentiment data on articles

Intellectual Property Summary:

Patented

Stage of Development:

7,996,210 8,515,739

Licensing Status:

Available for License.

Licensing Potential:

Licensing

Additional Information: