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74 lines
1.9 KiB
Python
74 lines
1.9 KiB
Python
from __future__ import print_function
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from __future__ import unicode_literals
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from builtins import str, bytes, dict, int
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import os
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import sys
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import codecs
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", ".."))
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import pattern.text as text_module
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from pattern.text.en.wordlist import STOPWORDS
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paths = []
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for f in os.listdir('./texts'):
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paths.append('./texts/' + f)
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texts = []
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for p in paths:
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with codecs.open(p, "rb", encoding='latin-1') as f:
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if sys.version_info[0] < 3:
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texts.append(f.read())
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else:
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texts.append(str(f.read()))
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ng = text_module.train_topmine_ngrammer(texts, threshhold=1, regexp="[^a-zA-Z0-9]")
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ngrams = text_module.topmine_ngramms(texts[0], ng, threshhold=1)
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print("\n")
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bigrams = []
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trigrams = []
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for key in ngrams.keys():
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if len(key.split("_")) == 2:
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bigrams.append(key)
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elif len(key.split("_")) == 3:
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trigrams.append(key)
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print("Extracted {} bigrams:\n".format(len(bigrams)))
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print(bigrams)
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print("\n")
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print("Extracted {} trigrams:\n".format(len(trigrams)))
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print(trigrams)
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print("\n")
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# as we can see the extracted ngrams contain many stopwords, so, it's important to delete all
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# stopwords before applying the algorythm
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ng = text_module.train_topmine_ngrammer(texts, threshhold=1, regexp="[^a-zA-Z0-9]", stopwords=STOPWORDS)
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ngrams = text_module.topmine_ngramms(texts[0], ng, threshhold=1)
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# as we can see the extracted ngrams contain many stopwords, so, it's important to delete all
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# stopwords before applying the algorythm
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print("\n")
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bigrams = []
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trigrams = []
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for key in ngrams.keys():
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if len(key.split("_")) == 2:
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bigrams.append(key)
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elif len(key.split("_")) == 3:
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trigrams.append(key)
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print("Extracted {} bigrams (removed stopwords):\n".format(len(bigrams)))
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print(bigrams)
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print("\n")
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print("Extracted {} trigrams (removed stopwords):\n".format(len(trigrams)))
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print(trigrams)
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print("\n")
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