Editing Final Report/Thesis 2015 (section)

=====Processing Search Results=====
Once the raw results were obtained, some grams contained words followed by an underscore and the corresponding lexical category of the word (ie. noun, verb, adverb, adjective, pronoun etc.).  This was desired to be removed and so another python code was written to remove everything but the words themselves from each line in the results.

Upon processing the raw results, the output of the lexical category removal results showed multiple identical results with individual frequencies for the numbers of years in which they occurred.  This was brought about since previously the database considered these entries to be unique results, but now with the lexical categories removed, some results became identical.  This was rectified by writing another code in Python to process these cleaned results to combine identical entries and sum their frequency of years in which they occurred.  This code was then duplicated and modified into two codes, the first output the results sorted alphabetical order, and the second in order of frequency of years in which each result occurred from highest to lowest.  The alphabetically sorted outputs were used as a means of comparison to the cleaned inputs since these were also sorted alphabetically, in order to check that the code was functioning correctly. The frequency sorted outputs were more useful since they able to be used to generate a condensed list of the top 30 most popular ''initialisms'' that could be generated from the letters from all variations in the Somerton Man code, seen in the results section in Figure 24.