A particular instance is "Queens Stop 'N' Swap". After transforming, I only got three features ['Queens', 'Stop', 'SWap']. The 'N' has been ignored. How can I capture the 'N'?. All the parameters are default settings in my code.
### Create the vectorizer method
tfidf_vec = TfidfVectorizer()
### Transform the text into tf-iwine vectors
text_tfidf = tfidf_vec.fit_transform(title_text)
You're not getting 'n' as a token because it's not considered a token by default tokenizer:
from sklearn.feature_extraction.text import TfidfVectorizer
texts = ["Queens Stop 'N' Swap",]
tfidf = TfidfVectorizer(token_pattern='(?u)\\b\\w\\w+\\b',)
tfidf.fit(texts)
tfidf.vocabulary_
{'queens': 0, 'stop': 1, 'swap': 2}
To capture 1 letter tokens, with capitalzation preserved, change it like:
tfidf = TfidfVectorizer(token_pattern='(?u)\\b\\w+\\b',lowercase=False)
tfidf.fit(texts)
tfidf.vocabulary_
{'Queens': 1, 'stop': 2, 'N': 0, 'swap': 3}