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The availability of big data has opened up big opportunities for individuals,businesses and academics to view big into what is happening in their world.Previous works of text representation mostly focused on informativeness frommassive words' frequency or cooccurrence. However, big data is a double-edgedsword which is big in volume but unstructured in format. The unstructured edgerequires specific techniques to transform 'big' into meaningful instead ofinformative alone.  This study presents KeypartX, a graph-based approach to represent perception(text in general) by key parts of speech. Different frombag-of-words/vector-based machine learning, this technique is human-likelearning that could extracts meanings from linguistic (semantic, syntactic andpragmatic) information. Moreover, KeypartX is big-data capable but not hungry,which is even applicable to the minimum unit of text:sentence.

KeypartX: Graph-based Perception (Text) Representation