I am newly getting into NLP, Python, and posting on Stackoverflow at the same time, so please be patient with me if I might seem ignorant :).
I am using SnowballStemmer in Python's NLTK in order to stem words for textual analysis. While lemmatization seems to understem my tokens, the snowball porter2 stemmer, which I read is mostly preferred to the basic porter stemmer, overstems my tokens. I am analyzing tweets including many names and probably also places and other words which should not be stemmed, like: hillary, hannity, president, which are now reduced to hillari, hanniti, and presid (you probably guessed already whose tweets I am analyzing).
Is there an easy way to exclude certain terms from stemming? Conversely, I could also merely lemmatize tokens and include a rule for common suffixes like -ed, -s, …. Another idea might be to merely stem verbs and adjectives as well as nouns ending in s. That might also be close enough…
I am using below code as of now:
# LEMMATIZE AND STEM WORDS
from nltk.stem.snowball import EnglishStemmer
lemmatizer = nltk.stem.WordNetLemmatizer()
snowball = EnglishStemmer()
def lemmatize_text(text):
return [lemmatizer.lemmatize(w) for w in text]
def snowball_stemmer(text):
return [snowball.stem(w) for w in text]
# APPLY FUNCTIONS
tweets['text_snowball'] = tweets.text_processed.apply(snowball_stemmer)
tweets['text_lemma'] = tweets.text_processed.apply(lemmatize_text)
I hope someone can help… Contrary to my past experience with all kinds of issues, I have not been able to find adequate help for my issue online so far.
Thanks!
