Accurate Recasting of Giant Text into Charts Using Rapid Automatic Keyword Extraction Algorithm in Comparison with Bag of Words Algorithm

Aim: For contrasting the accuracy of understandability between the techniques Novel Rapid Automatic Keyword Extraction (RAKE) and BOW (Bag Of Words) in the NLP (Natural Language Processing) utilizing the corpus Donald trumph speeches for building squarified charts from big text, that is the central goal. Materials and Methods: After pulling off key phrases from the huge content in a document, the rate of accuracy is evaluated. The accuracy rate evaluated based upon the quantity of similar key phrases in Novel RAKE and BOW compared with manual allotted key phrases. The key phrases grabbing done utilizing the Novel RAKE and BOW with sample sizes each 28 in the NLP (Natural Language Processing) resulting in G-power value 80%. Results: The Novel Rapid Automatic Keyword Extraction (RAKE) shows a larger rate of accuracy of 73.04% than the BOW (Bag Of Words) of 69.04% in gathering key phrases from a text file. This indicates that there is no statistically significant difference between the Novel RAKE algorithm and the BOW method with
$\mathrm{p}=0.333(\mathrm{p} > 0.05)$
. Conclusion: The Novel RAKE has a larger rate of prediction accuracy of 73.04% when distinguished with the rate of prediction accuracy of 69.04% in BOW and subset of parameters or attributes in the marginal distribution.