59 lines
2.1 KiB
Python
59 lines
2.1 KiB
Python
'''
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Filter Keywords
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===============
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FilterKeywords searches for merger specific keywords
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in an article and counts them.
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'''
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# toDo: replace dict by vector/matrix
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from collections import defaultdict
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import re
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from nltk.stem.porter import PorterStemmer
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class FilterKeywords:
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def search_keywords(dict_input):
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'''extracts relevant key-value pairs of in article's input dictionary,
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output are the contained keywords and their count.
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'''
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keyword_list = ['merge', 'merges', 'merged', 'merger', 'mergers',
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'acquisition', 'acquire', 'acquisitions', 'acquires',
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'combine', 'combines', 'combination', 'combined',
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'joint', 'venture', 'JV', 'takeover', 'take-over',
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'tie-up', 'deal', 'deals', 'transaction',
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'transactions', 'approve', 'approves', 'approved',
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'approving', 'approval', 'approvals', 'buy', 'buys',
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'buying', 'bought', 'buyout', 'buy-out', 'purchase',
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'sell', 'sells', 'selling', 'sold', 'seller', 'buyer']
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# reduce words to stem
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stemmer = PorterStemmer()
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for i in range(len(keyword_list)):
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keyword_list[i] = stemmer.stem(keyword_list[i])
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# remove duplicates
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keywords = set(keyword_list)
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# # counts keywords in article (default value: 0)
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# dict_keywords = defaultdict(int)
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# # search for matchings in dictionary of input article
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# for key in dict_input.keys():
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# # iterate over all regular expressions
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# for kword in keywords:
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# if re.match(kword, key):
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# # if match, increase value of matching key
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# if str(kword) in dict_keywords:
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# dict_keywords[str(kword)] += dict_input[key]
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# else:
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# dict_keywords[str(kword)] = dict_input[key]
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# return dict_keywords
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if __name__ == '__main__':
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# dict_test={'example':2, 'combined':5, 'sells':3}
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# print(FilterKeywords.search_keywords(dict_test)) |