I have a dataset of profiles that contain freeform text describing the work history of a number of individuals.
I would like to attempt to identify frequently used words or groups of words across the set of profiles in order that I can build a taxonomy (of skills) related to the profiles.
For example, if the words 'conversion rate optimisation' appear together 300 times across all profiles, I would see this on my list as a high-frequency keyphrase. I would expect to be able to filter the list based on single keywords, 2 words, and 3-word strings.
I would then be able to manually pick out frequently used keyphrases relating to skills, that could be added to a master taxonomy list.
I would also need some way of filtering out invalid words like ('I', 'and' etc)
What is the best way to get something like this done?