Today's #BigData world, #analytics took additional complexity beyond pure statistics or pattern recognition using clustering, segmentation or predictive analytics using logistic regression methods.
One of the great challenge for big data's unstructured analytics is the 'context'. In traditional processing of data, we have removed the context and just recorded the content. All the we try to do with sentiment analysis is based on deriving the words, phrases & entities and try to combine them into 'concepts' and score them by matching known patterns of pre-discovered knowledge and assign the sentiment to the content.
The success rate in this method is fairly low. (This is my own personal observation!) One of the thoughts to improve the quality of this data is to add the context back to the content. To do this the technology enables is again a 'Big Data' solution. Means, we start with a big data problem and find the solution in the big data space. Interesting. Isn't it?
Take the content data at rest, analyze it. and enrich with the context information like spatial and temporal information and derive knowledge from it. Visualize the data by putting similar concepts together and by merging same concepts into a single entity.
The big blue is doing this after realizing the fact. Few months back they published a 'red paper' that can be found here.
Finally putting the discovered learning into action in real time gives all the needed business impact and takes it to the world of Analytics 3.0. (Refer to http://iianalytics.com/a3/)
Exciting world of opportunities....
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