27 lines
824 B
Python
27 lines
824 B
Python
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import nltk
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from nltk.corpus import wordnet
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from nltk.corpus import stopwords
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from nltk import FreqDist
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# Download necessary resources
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nltk.download('wordnet')
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nltk.download('stopwords')
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# Load all the adjectives from WordNet
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adjectives = set([synset.lemmas()[0].name() for synset in wordnet.all_synsets(wordnet.ADJ)])
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# Filter out stopwords
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stop_words = set(stopwords.words('english'))
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adjectives = [adjective for adjective in adjectives if adjective not in stop_words]
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# Calculate the frequency distribution of adjectives
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fdist = FreqDist(adjectives)
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# Get the 4000 most common adjectives
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most_common_adjectives = fdist.most_common(4000+1)
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# Output the adjectives to a text file
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with open('english_adjectives.txt', 'w') as file:
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file.write('\n'.join([adjective for adjective, count in most_common_adjectives]))
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