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        "author": "Everton Tomalok",
        "author_email": "evertontomalok123@gmail.com",
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            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3"
        ],
        "description": "# preprocessingtext\n \nA tool short, but very usefull to help in pre-processing data from texts.\n\n \n\n## How to Install\n \n\n    >> pip install --user preprocessingtext\n    \n \n\n## Usage\n\n#### Using stem_sentence()\n\n    >> from preprocessingtext import CleanSentence\n    \n    >> cleaner =  CleanSentence(idiom='portuguese')\n    \n    >> cleaner.stem_sentence(sentence=\"String\", remove_stop_words=True, remove_punctuation=True, normalize_text=True, replace_garbage=True)\n\nTo init a a class, you need to pass the idiom that you want to work. The custom value, is \"portuguese\".  \n\nBefore, you can instance a new object from CleanSentence, and call the method stem_sentence. You can choose in use \n\"remove_stop_words\" from string (pass True or False) and \"remove_punctuation\" from string (pass True or False),  \n\"replace_garbage\" (True or False) removing values from data, and \"normalize_text\" (True or False) to normalize text.\n   \n#### Usage of list_to_replace\nYou can improve what you need to replace (clean) in your data.  You can use \"cleaner.list_to_replace.append('what_you_need_to_add')\",\nor you can pass a new list of values:  cleaner.list_to_replace = ['item1', 'item2', 'item3']\n\n    # Custom value of list_to_replace\n    >> list_to_replace = ['https://', 'http://', '$']\n    \n    # Adding new values\n    list_to_replace.append('item1')\n    ['https://', 'http://', 'R$', '$', 'item1']\n    \n    # Replacing values\n    >> list_to_replace = ['item1', 'item2', 'item3']\n   \n\n#### Using tokenizer()\n\n    >> cleaner.tokenizer('Um exemplo de tokens.')\n    \n    >> ['Um', 'exemplo', 'de', 'tokens']\n\n## Example\n    \n    ## Using all parameters of stem_sentence()\n    >> string = \"Eu sou uma senten\u00e7a comum. Serei pr\u00e9-processada com este modulo, veremos a serguir usando os m\u00e9todos disponiveis\"\n    >> cleaner.stem_sentence(sentence=string,\n                             remove_stop_words=True,\n                             remove_punctuation=True,\n                             normalize_text=True,\n                             replace_garbage=True\n                            )\n    >> sentenc comum pre-process modul ver segu us metod disponi\n    \n    ## Don't using remove_stop_words\n    >> print(cleaner.stem_sentence(sentence=string,\n                            remove_stop_words=False,\n                            remove_punctuation=True,\n                            normalize_text=True,\n                            replace_garbage=True\n                            )\n      )\n    >> eu sou uma sentenc comum ser pre-process com est modul ver a segu us os metod disponi\n    \n    ## Tokenizer\n    >> print(cleaner.tokenizer('Um exemplo de tokens.'))\n    >> ['Um', 'exemplo', 'de', 'tokens']\n    \n    ## Cleaning garbage words\n    >> string_web = 'Acesse esses links para ganhar dinheiro: https://easymoney.com.net and http://falselink.com'\n    >> cleaner.stem_sentence(sentence=string_web,\n                             remove_stop_words=False,\n                             remove_punctuation=True,\n                             replace_garbage=True\n                            )\n    >> acess ess link par ganh dinh easymoney.com.net and falselink.com\n            \n## English example\n    >> en_cleaner = CleanSentences(idiom='english')\n    \n    >> string_web = 'Access these links to gain money: https://easymoney.com.net and http://falselink.com'\n    >> print(en_cleaner.stem_sentence(sentence=string_web,\n                                      remove_stop_words=True,\n                                      remove_punctuation=True,\n                                      replace_garbage=True\n                                      )\n            )     \n    >> acc link gain money easymoney.com.net falselink.com\n\n   \n# Author\n{\n 'name': Everton Tomalok, \n'email': evertontomalok123@gmail.com \n}",
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        "keywords": "pre-processing text",
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        "summary": "A series of methods to help you work pre processing of text in general, like stem, tokenizer and others.",
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