iptc classification java code example
>>> client.set_classifiers(["textrazor_newscodes"])iptc classification java code example
>>> client.set_classifiers(["textrazor_newscodes"])iptc classification java code example
>>> response = client.analyze_url("http://www.bbc.co.uk/news/uk-politics-18640916")iptc classification java code example
>>> entities = list(response.entities())
>>> entities.sort(key=lambda x: x.relevance_score, reverse=True)
>>> seen = set()
>>> for entity in entities:
>>> if entity.id not in seen:
>>> print entity.id, entity.relevance_score, entity.confidence_score, entity.freebase_types
>>> seen.add(entity.id)
Debt 0.720049 1.53685 ['/media_common/quotation_subject', '/book/book_subject']
Barclays 0.705099 1.77573 []
Bank 0.635486 1.35011 ['/book/book_subject', '/architecture/building_function']
Mervyn King (economist) 0.619704 2.26693 ['/people/person', '/government/politician', '/business/board_member']
Bill (proposed law) 0.619034 1.03724 ['/tv/tv_subject']
Chancellor of the Exchequer 0.607768 0.952038 ['/government/government_office_or_title']
David Cameron 0.5875 4.18796 ['/people/person', '/film/actor', '/government/politician', '/tv/tv_actor', '/film/person_or_entity_appearing_in_film']
Risk 0.563535 0.98141 ['/media_common/quotation_subject', '/book/book_subject']
Citigroup 0.444129 3.39143 ['/business/employer', '/venture_capital/venture_investor', '/business/sponsor', '/award/ranked_item', '/business/issuer', '/business/business_operation', '/organization/organization', '/business/board_member', '/organization/organization_partnership']
Libor 0.436299 2.08194 []iptc classification java code example
>>> for topic in response.topics():
>>> if topic.score > 0.3:
>>> print topic.label
Bank
Libor
Barclays
Politics
Economics
Government
Banking
Business
Bank of England
Financial services
.....iptc classification java code example
>>> import textrazor
>>> textrazor.api_key = "API_KEY_GOES_HERE"
>>>
>>> client = textrazor.TextRazor(extractors=["entities", "topics"])iptc classification java code example
>>> for category in response.categories():
>>> print category.category_id, category.label, category.score
11000000 politics 0.936274
11006000 politics>government 0.899296
11024000 politics>politics (general) 0.856208
04017000 economy, business and finance>economy (general) 0.772154
08003000 human interest>people 0.661979
12006001 religion and belief>values>ethics 0.650902
04018000 economy, business and finance>business (general) 0.650722
14025000 social issue>social issues (general) 0.637389
04006000 economy, business and finance>financial and business service 0.637093
14000000 social issue 0.5628
04006002 economy, business and finance>financial and business service>banking 0.561675
.....iptc classification java code example
>>> client.set_cleanup_mode("cleanHTML")Copyright © 2021 Codeinu
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