azure-rong / review-helpfulness-prediction Goto Github PK
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Project of automatically detecting review helpfulness. Using
1.when you are selecting the best features you should not look into your test data.
2.I think the feature vector of a sentence should include all the words in 'best_words' set, instead of just the words in that sentence.
#1. Load dictionary and dataset
posdict = tp.get_txt_data("D:/code/sentiment_dictionary/posdict.txt","lines")
negdict = tp.get_txt_data("D:/code/sentiment_dictionary/negdict.txt","lines")
mostdict = tp.get_txt_data('D:/code/sentiment_dictionary/most.txt', 'lines')
verydict = tp.get_txt_data('D:/code/sentiment_dictionary/very.txt', 'lines')
moredict = tp.get_txt_data('D:/code/sentiment_dictionary/more.txt', 'lines')
ishdict = tp.get_txt_data('D:/code/sentiment_dictionary/ish.txt', 'lines')
insufficientdict = tp.get_txt_data('D:/code/sentiment_dictionary/insufficiently.txt', 'lines')
inversedict = tp.get_txt_data('D:/code/sentiment_dictionary/inverse.txt', 'lines')
我找不到这些txt文件,能不能更新一下?
@Azure-rong
Sir
about function def cut_sentences_1 in textprocessing.py
has a word of""" Maybe this algorithm will have bugs in it """
I want to know about this bugs.
Can you give me some example about this bugs??
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