Language Detection in Python with NLTK Stopwords

Lately I've been coding a little more Python than usual, some twitter API stuff, some data crunching code.  The other day I was thinking how I could detect the language a twitter user was writing in. Of course, I'm sure there is a library out there that does it... But the NLTK library (the Natural Language Toolkit for Python) does not have any function for this, or at least I was not able to find it after 5 minutes of Google search. So...

I had a simple enough idea to determine it, though. NLTK comes equipped with several stopword lists. A stopword is a very common word in a language, adding no significative information ("the" in English is the prime example. My idea: pick the text, find most common words and compare with stopwords. The language with the most stopwords "wins".

Implementing it was just a matter of a few minutes and around 45 lines.

from nltk.corpus import stopwords

def scoreFunction(wholetext):
    """Get text, find most common words and compare with known
    stopwords. Return dictionary of values"""
    # C makes me program like this: create always empty stuff just in case
    # These are the available languages with stopwords from NLTK
    # Just in case I add stopword lists
    # Fill the dictionary of languages, to avoid  unnecessary function calls
    for lang in NLTKlanguages:
    # Split all the text in tokens and convert to lowercase. In a
    # decent version of this, I'd also clean the unicode
    tokens=[t.lower() for t in tokens]
    # Determine the frequency distribution of words, looking for the
    # most common words
    # This is the only interesting piece, and not by much. Pick a
    # language, and check if each of the 20 most common words is in
    # the language stopwords. If it's there, add 1 to this language
    # for each word matched. So the maximal score is 20. Why 20? No
    # specific reason, looks like a good number of words.
    for lang in languages:
        for word in freq_dist.keys()[0:20]:
            if word in dictiolist[lang]:
    return scorelist

def whichLanguage(scorelist):
    """This function just returns the language name, from a given
    "scorelist" dictionary as defined above."""
    for item in scorelist:
        if maximum<value:
    return lang

Well, does it work? Quite! I tested it with some Wikipedia text:

scoreFunction("e Operationen in der Karibik, ohne dass es dabei zu größeren See­schlachten gekommen wäre. In Europa war die erfolglose Belagerung des britischen Stütz­punktes Gibraltar die einzige nennens­werte Auseinander­setzung. Der englisch-­spanische Konflikt endete formell am 9. November 1729 mit dem Abschluss des Vertrages von Sevilla und der Wieder­herstellung des Status quo ante. Die grundsätzlichen Differenzen beider Staaten wurden jedoch nicht beseitigt, was kaum zehn Jahre später zum Ausbruch eines weiteren Krieges führte")
{'swedish': 0, 'portuguese': 0, 'english': 2, 'hungarian': 0, 'finnish': 0, 'turkish': 0, 'german': 5, 'dutch': 3, 'french': 1, 'norwegian': 1, 'catalan': 0, 'spanish': 0, 'russian': 0, 'danish': 1, 'italian': 1}
scoreFunction("Man vet forholdsvis lite om Merkur; bakkebaserte teleskop viser kun en opplyst halvmåne med begrensede detaljer. Mye av informasjonen om planeten ble samlet av Mariner 10 (1974–76) som kartla rundt 45 % av overflaten.")
{'swedish': 3, 'portuguese': 0, 'english': 0, 'hungarian': 0, 'finnish': 1, 'turkish': 1, 'german': 0, 'dutch': 2, 'french': 1, 'norwegian': 4, 'catalan': 1, 'spanish': 1, 'russian': 0, 'danish': 2, 'italian': 0}
scoreFunction("A transit of Venus across the Sun takes place when the planet Venus passes directly between the Sun and Earth, becoming visible against the solar disk. During a transit, Venus can be seen from Earth as a small black disk moving slowly across the face of the Sun")
{'swedish': 0, 'portuguese': 2, 'english': 9, 'hungarian': 2, 'finnish': 0, 'turkish': 0, 'german': 0, 'dutch': 1, 'french': 1, 'norwegian': 0, 'catalan': 1, 'spanish': 1, 'russian': 0, 'danish': 0, 'italian': 1}

But it breaks with non-ascii text (like accents, umlauts and other funny letters,) so it is quite un-useful in these cases. But oh well, for 10 minutes of coding it's not that bad, a quick hack.

Since last week I had started to read the django book, I thought this would make for an interesting first project to post online, and you can find it at whatlanguageis.com, with some unicode improvements. It's still in very early beta, working with just a handful of languages and without any kind of text-length checker. Just a proof of concept about my django "skills."
Written by Ruben Berenguel