http://blog.alejandronolla.com/2013/05/15/detecting-text-language-with-python-and-nltk/
>>> from nltk import wordpunct_tokenize
>>> wordpunct_tokenize("That's thirty minutes away. I'll be there in ten.")
['That', "'", 's', 'thirty', 'minutes', 'away', '.', 'I', "'", 'll', 'be', 'there', 'in', 'ten', '.']
>>> from nltk.corpus import stopwords
>>> stopwords.fileids()
['danish', 'dutch', 'english', 'finnish', 'french', 'german', 'hungarian', 'italian', 'norwegian', 'portuguese', 'russian', 'spanish', 'swedish', 'turkish']
>>>
>>> stopwords.words('english')[0:10]
['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', 'your']
>>> languages_ratios = {}
>>>
>>> tokens = wordpunct_tokenize(text)
>>> words = [word.lower() for word in tokens]
>>> for language in stopwords.fileids():
... stopwords_set = set(stopwords.words(language))
... words_set = set(words)
... common_elements = words_set.intersection(stopwords_set)
...
... languages_ratios[language] = len(common_elements)
# language "score"
>>>
>>> languages_ratios
{'swedish': 1, 'danish': 1, 'hungarian': 2, 'finnish': 0, 'portuguese': 0, 'german': 1, 'dutch': 1, 'french': 1, 'spanish': 0, 'norwegian': 1, 'english': 6, 'russian': 0, 'turkish': 0, 'italian': 2}
>>> most_rated_language = max(languages_ratios, key=languages_ratios.get)
>>> most_rated_language
'english'