TFIDF for very short sentences I'm trying to build a regression model, in which one of the features contains text data. I was thinking in using scikit-learn's  sklearn.feature_extraction.text.TfidfVectorizer. The issue however, is that the actual strings contain very few words. Exactly 1.8 in average. Here's a sample:

 print(df.keyword)

0 fre lifeproof
1 car stereo
2 analog clock
3 refrigerator


So my question is, 


is the TfidfVectorizer also suited for such case? Or will such sparse resulting matrix not be benefiting the model? 
Is there a more suited approach for such case?
It depends what it's going to be used for, but in general it can make sense to use TF-IDF with short sentence. The main difference with the more standard case of long sentences is that TF (Term Frequency) won't play any role since the frequency will almost always be 1. IDF can still be useful though, assuming it's relevant to assign more weight to rare words than to frequent words. 

However the problem is that comparisons such as cosine are often going to be zero, since there will be little chance of words in common between two sentences.