Text Analytics FrameWork


We have an unyielding focus on Business outcomes and value creation, both from Business and IT perspectives. We have simple, “no-sales” pitch, unbiased, “no-fluff” and unnecessary deliverables philosophy to our consulting approach. All our offerings will have the map to the objectives listed below


Collect:

The data collected is raw in nature and collected form various sources. Pre processing is done by removing stop words and reference words that are not pertinent in analysis. A porter stemming algorithm is run to stem the word to its root ( thus yielding data that gives meaningful format for further analysis). Data Sourced from:

Analyze:

The data is processed with various classifiers. It considers each of these features to contribute independently and assumes that the presence and absence of a particular feature is unrelated to the presence or absence of any feature. This will be enriched with Meta data

Index:

We start the step with issue exploration and enable key word search with linguistic analysis. Linguistics-based text analytics is based on natural language processing (NLP). The NLP approach cuts through the ambiguity of text, making linguistics-based text analytics the most accurate possible approach.

Semantic enrichment is the process of creating or associating semantic tags in unstructured data or text, usually involving concepts, entities, relationships, events and properties described in an ontology or rule based