Which features do I select from text?
Using NLTK in python you should first Tokenize the sentences into words, even you can use Ngram for 2-Gram or 3-Gram bags of word, the reason I am suggesting N-Gram is that let's suppose you have sentence like: I am not happy with this product, then 2-Gram tokenize it as ['not happy', 'happy with', 'with this', 'this product'] here I and am are assumed as STOPWORDS. Using HashingTF you can hash the sentence into a feature vector as ['word position': frequency of word, ...] i.e highly sparse vectors, For Hashing in PySpark check this documentation. 

Here below python code will help you to tokenize in bags of word

import string

from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer

PUNCTUATION = set(string.punctuation)
STOPWORDS = set(stopwords.words('english'))
STEMMER = PorterStemmer()

example = ['Hello Krishna Prasad, this is test file for spark testing',
            'Another episode of star',
            'There are far and away many stars'
            'A galloping horse using two coconuts'
            'My kingdom for a horse'
            'A long time ago in a galaxy far']

def tokenize(text):
    tokens = word_tokenize(text)
    lowercased = [t.lower() for t in tokens]
    no_punctuation = []
    for word in lowercased:
        punct_removed = ''.join([letter for letter in word if not letter in PUNCTUATION])
        no_punctuation.append(punct_removed)
    no_stopwords = [w for w in no_punctuation if not w in STOPWORDS]
    stemmed = [STEMMER.stem(w) for w in no_stopwords]
    return [w for w in stemmed if w]

tokenized_word = [tokenize(text) for text in example]
for word in tokenized_word:
    print word


Out of the above code as:

$python WordFrequencyHash.py
[u'hello', u'krishna', u'prasad', u'test', u'file', u'spark', u'test']
[u'anoth', u'episod', u'star']
[u'far', u'away', u'mani', u'starsa', u'gallop', u'hors', u'use', u'two', u'coconutsmi', u'kingdom', u'horsea', u'long', u'time', u'ago', u'galaxi', u'far']


You can also use word2vec or countvectorizer for tokenization.