sophie4869/topic-model
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---.txt--- dict.txt: vocabulary for feature extraction chi_n.txt: Chinese stopwords separated by '\n' chi_,.txt: Chinese stopwords separated by ',' chi_,.backup: same as chi_,.txt features_10000.txt: 10000 most common words from 26 data files features_5000.txt: 5000 most common words from e-1 data files jieba_cut_result.txt: jieba cut result of e-1.json stanford_cut_result.txt: stanford cut result of ? extraction.txt: e-1.json text content all together jieba_pw_compareResult.txt: RT output.txt: ? cut result(very large) test*.txt: test text and its cutting result ---.py--- read.py: getText from .json file bow.py: bag of words for jieba tfidf.py: tfidf function tfidf_cmd.py: run tfidf from command line classify.py: classify based on bow addstop.py: add stop word to three chi*.txt files test.py: pwCount, jiebaCount, tfidf function t.py: test word2vec.py: word2vec using gensim ---.other--- jieba.model: Word2Vec model generated using jieba cutting
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