How can I text-mine full-text articles from PubMed?
Below is a Python script that might help you to get started  (apologies if it fails the Pythonic test - it works!). It uses the Entrez part of the Biopython library. The script sets up a query, in this case yeast AND Saccharomyces against the pmc database. Also note that this script uses the 2 step process that NCBI likes you to use - the first part of the fetchByQuery function gets a set of results then the second part uses those results to actually obtain the data. 

The output is an xml file which you get to parse with your favourite tools. In your case you will need to get out the text sections and do your token analysis. If you use Python for that I recommend the Natural Language Toolkit (NLTK).

In your case you could just set up search terms as a Python list and loop through writing each dataset to a file named from the search term.

from Bio import Entrez

import urllib 
import urllib2
import sys

def fetchByQuery(query,days):
    Entrez.email = "xxx" # you must give NCBI an email address
    searchHandle=Entrez.esearch(db="pmc", reldate=days, term=query, usehistory="y")
    searchResults=Entrez.read(searchHandle)
    searchHandle.close()
    webEnv=searchResults["WebEnv"]
    queryKey=searchResults["QueryKey"]
    batchSize=10
    try:
        fetchHandle = Entrez.efetch(db="pmc", retmax=100, retmode="xml", webenv=webEnv, query_key=queryKey)
        data=fetchHandle.read()
        fetchHandle.close()
        return data
    except:
        return None

days=100 #looking for papers in the last 100 days
termList=["yeast","Saccharomyces"] 

query=" AND ".join(termList)
xml_data=fetchByQuery(query,days)
if xml_data==None: 
    print 80*"*"+"\n"
    print "This search returned no hits"

else:
    f=open("pmcXml.txt" ,"w")
    f.write(xml_data)
    f.close()