Przykład klasyfikacji tekstu IPTC
#! /usr/bin/env python
# Created by MeaningCloud Support Team
# Date: 26/02/18
import sys
import meaningcloud
# @param model str - Name of the model to use. Example: "IAB_en" by default = "IPTC_en"
model = 'IAB_en'
# @param license_key - Your license key (found in the subscription section in https://www.meaningcloud.com/developer/)
license_key = '<<<<< your license key >>>>>'
# @param text - Text to use for different API calls
text = 'London is a very nice city but I also love Madrid.'
try:
# We are going to make a request to the Topics Extraction API
topics_response = meaningcloud.TopicsResponse(meaningcloud.TopicsRequest(license_key, txt=text, lang='en',
topicType='e').sendReq())
# If there are no errors in the request, we print the output
if topics_response.isSuccessful():
print("\nThe request to 'Topics Extraction' finished successfully!\n")
entities = topics_response.getEntities()
if entities:
print("\tEntities detected (" + str(len(entities)) + "):\n")
for entity in entities:
print("\t\t" + topics_response.getTopicForm(entity) + ' --> ' +
topics_response.getTypeLastNode(topics_response.getOntoType(entity)) + "\n")
else:
print("\tNo entities detected!\n")
else:
if topics_response.getResponse() is None:
print("\nOh no! The request sent did not return a Json\n")
else:
print("\nOh no! There was the following error: " + topics_response.getStatusMsg() + "\n")
# CLASS API CALL
# class_response = meaningcloud.ClassResponse(
# meaningcloud.ClassRequest(license_key, txt=text, model=model).sendReq())
# SENTIMENT API CALL
# sentiment_response = meaningcloud.SentimentResponse(
# meaningcloud.SentimentRequest(license_key, lang='en', txt=text, txtf='plain').sendReq())
# GENERIC API CALL
# generic = meaningcloud.Request(url="url_of_specific_API",key=key)
# generic.addParam('parameter','value')
# generic_result = generic.sendRequest()
# generic_response = meaningcloud.Response(generic_result)
# We are going to make a request to the Language Identification API
lang_response = meaningcloud.LanguageResponse(meaningcloud.LanguageRequest(license_key, txt=text).sendReq())
# If there are no errors in the request, we will use the language detected to make a request to Sentiment and Topics
if lang_response.isSuccessful():
print("\nThe request to 'Language Identification' finished successfully!\n")
first_lang = lang_response.getFirstLanguage()
if first_lang:
language = lang_response.getLanguageCode(first_lang)
print("\tLanguage detected: " + lang_response.getLanguageName(first_lang) + ' (' + language + ")\n")
else:
print("\tNo language detected!\n")
# We are going to make a request to the Lemmatization, PoS and Parsing API
parser_response = meaningcloud.ParserResponse(
meaningcloud.ParserRequest(license_key, txt=text, lang='en').sendReq())
# If there are no errors in the request, print tokenization and lemmatization
if parser_response.isSuccessful():
print("\nThe request to 'Lemmatization, PoS and Parsing' finished successfully!\n")
lemmatization = parser_response.getLemmatization(True)
print("\tLemmatization and PoS Tagging:\n")
for token, analyses in lemmatization.items():
print("\t\tToken -->", token)
for analysis in analyses:
print("\t\t\tLemma -->", analysis['lemma'])
print("\t\t\tPoS Tag -->", analysis['pos'], "\n")
except ValueError:
e = sys.exc_info()[0]
print("\nException: " + str(e))
Clear Crayfish
