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@ -34,6 +34,15 @@ def test_languages():
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assert_greater(word_frequency('lol', new_lang_code), 0)
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def test_twitter():
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avail = available_languages('twitter')
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assert_greater(len(avail), 12)
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for lang in avail:
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assert_greater(word_frequency('rt', lang, 'twitter'),
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word_frequency('rt', lang, 'combined'))
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def test_defaults():
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eq_(word_frequency('esquivalience', 'en'), 0)
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eq_(word_frequency('esquivalience', 'en', default=1e-6), 1e-6)
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@ -331,9 +331,8 @@ def half_harmonic_mean(a, b):
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def word_frequency(word, lang, wordlist='combined', default=0.):
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"""
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Get the frequency of `word` in the language with code `lang`, from the
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specified `wordlist`. The default (and currently only) wordlist is
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'combined', built from whichever of these four sources have sufficient
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data for the language:
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specified `wordlist`. The default wordlist is 'combined', built from
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whichever of these four sources have sufficient data for the language:
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- Full text of Wikipedia
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- A sample of 72 million tweets collected from Twitter in 2014,
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@ -341,6 +340,9 @@ def word_frequency(word, lang, wordlist='combined', default=0.):
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- Frequencies extracted from OpenSubtitles
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- The Leeds Internet Corpus
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Another available wordlist is 'twitter', which uses only the data from
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Twitter.
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Words that we believe occur at least once per million tokens, based on
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the average of these lists, will appear in the word frequency list.
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If you look up a word that's not in the list, you'll get the `default`
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