Use word stems for key term matching

[Mr0grog]

When we check whether any key terms have changed, we should try out using word stemming instead of exact matches:

web-monitoring-task-sheets/analyst_sheets/analyze.py

Lines 190 to 195 in 074cc01

	key_terms = {term: all_terms[term]
	for term in KEY_TERMS
	if abs(all_terms.get(term, 0)) > 0}
	key_terms_changed = len(key_terms) > 0
	key_terms_change_count = sum((abs(count)
	for term, count in key_terms.items()))

This should be something we can turn on/off, since I’m not sure how well it will work and whether we’ll get a lot of false positives.

To keep things comprehensible, we need to keep a map of "stemmed terms" → "actual terms" so that we can present them as the actual terms, even though we are matching by stem.

NLTK supports several different stemming implementations, so I need to do some reading and testing as to what makes the most sense. API docs: https://www.nltk.org/api/nltk.stem.html