I'm quite new to the NLTK package of Python and to NLP too (I usually work in R but for NLP purposes and scraping maybe Python is more able).
I scrap articles from Hungarian newsportals and want to make a wordcloud out of it to show what are the current trending news topics. First I filter out stopwords and then stem the remaining words. (nltk has Hungarian stemmer) So I'm able to make a frequency table which can be the base of the wordcloud. My problem comes afterwards because stems are usually meaningless chunks (and not lemmas) of real words. I want to somehow complete the stem to a real word.
My first idea was to assign the most common word or the shortest one (or some combination of this 2 rules) to the stem and represent that in the wordcloud.
Is there a better solution for stem completion or should I follow a different workflow?