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| [](https://pypi.org/project/rake-new2/)  [](https://github.com/BALaka-18/rake_new2/blob/master/LICENSE.txt)  | ||
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| [](https://github.com/BALaka-18/rake_new2/issues) [](https://github.com/BALaka-18/rake_new2/network/members) [](https://github.com/BALaka-18/rake_new2/stargazers) | ||
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| <p align="center"> | ||
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| <img src="https://embed-fastly.wistia.com/deliveries/49bd387c40e2c5aada92abdf973bc46d.webp?image_crop_resized=960x540"> | ||
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| </p> | ||
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| ### What is keyword extraction ? **[READ HERE](https://monkeylearn.com/keyword-extraction/)** | ||
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| # **ABOUT THE PROJECT** | ||
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| # rake_new2 | ||
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| rake_new2 is a Python library that enables simple and fast keyword extraction from any text. As the name implies, this library works on the RAKE(Rapid Automatic Keyword Extraction) algorithm. | ||
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| It tries to determine the key phrases in a text by calculating the co-occurrences of every word in a key phrase and also its frequency in the entire text. | ||
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|  | ||
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| ## New in version 1.0.5 | ||
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| 1. Handles repetitive keywords/key-phrases | ||
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| 2. Handles consecutive punctuations. | ||
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| 3. Handles HTML tags in text : The user is allowed an option to choose if they want to keep HTML tags as keywords too. | ||
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|  | ||
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| ## Installation | ||
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| Use the package manager [pip](https://pip.pypa.io/en/stable/) to install rake_new2. | ||
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| ```bash | ||
| pip install rake_new2 | ||
| ``` | ||
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| ## Quick Start | ||
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| ```python | ||
| from rake_new2 import Rake | ||
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| text = "Red apples are good in taste." | ||
| text2 = "<h1> Hello world !</h1>" | ||
| rk,rk_new1,rk_new2 = Rake(),Rake(keep_html_tags=True),Rake(keep_html_tags=False) | ||
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| # Case 1 | ||
| # Initialize | ||
| rk.get_keywords_from_raw_text(text) | ||
| kw_s = rk.get_keywords_with_scores() | ||
| # Returns keywords with degree scores : {(1.0, 'taste'), (1.0, 'good'), (4.0, 'red apples')} | ||
| kw = rk.get_ranked_keywords() | ||
| # Returns keywords only : ['red apples', 'taste', 'good'] | ||
| f = rk.get_word_freq() | ||
| # Returns word frequencies as a Counter object : {'red': 1, 'apples': 1, 'good': 1, 'taste': 1} | ||
| deg = rk.get_kw_degree() | ||
| # Returns word degrees as defaultdict object : {'red': 2.0, 'apples': 2.0, 'good': 1.0, 'taste': 1.0} | ||
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| # Case 2 : Sample case for testing the 'keep_html_tags' parameter. Default = False | ||
| print("\nORIGINAL TEXT : {}".format(text)) | ||
| # Sub Case 1 : Keeping the HTMLtags | ||
| rk_new1.get_keywords_from_raw_text(text2) | ||
| kw_s1 = rk_new1.get_keywords_with_scores() | ||
| kw1 = rk_new1.get_ranked_keywords() | ||
| print("Keeping the tags : ",kw1) | ||
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| # Sub Case 2 : Eliminating the HTML tags | ||
| rk_new2.get_keywords_from_raw_text(text2) | ||
| kw_s2 = rk_new2.get_keywords_with_scores() | ||
| kw2 = rk_new2.get_ranked_keywords() | ||
| print("Eliminating the tags : ",kw2) | ||
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| '''OUTPUT >> | ||
| ORIGINAL TEXT : <h1> Hello world !</h1> | ||
| Keeping the tags : {'h1', 'hello'} | ||
| Eliminating the tags : {'hello world'} | ||
| ''' | ||
| ``` | ||
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| ## Debugging | ||
| You might come across a stopwords error. | ||
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| It implies that you do not have the stopwords corpus downloaded from NLTK. | ||
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| To download it, use the command below. | ||
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| ```python | ||
| python -c "import nltk; nltk.download('stopwords')" | ||
| ``` | ||
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| ## Contributing | ||
| Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change. | ||
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| Please make sure to update tests as appropriate. | ||
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| ## License | ||
| [MIT](https://choosealicense.com/licenses/mit/) | ||
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| ### Beta Tester | ||
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| Sankha Subhra Mondal | ||
| [](https://pypi.org/project/rake-new2/) | ||
|  | ||
| [](https://github.com/BALaka-18/rake_new2/blob/master/LICENSE.txt) | ||
|  | ||
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| [](https://github.com/BALaka-18/rake_new2/issues) | ||
| [](https://github.com/BALaka-18/rake_new2/network/members) | ||
| [](https://github.com/BALaka-18/rake_new2/stargazers) | ||
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| <p align="center"> | ||
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| <img src="https://embed-fastly.wistia.com/deliveries/49bd387c40e2c5aada92abdf973bc46d.webp?image_crop_resized=960x540"> | ||
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| </p> | ||
|
|
||
| ### What is keyword extraction ? **[READ HERE](https://monkeylearn.com/keyword-extraction/)** | ||
|
|
||
| # **ABOUT THE PROJECT** | ||
|
|
||
| # rake_new2 | ||
|
|
||
| rake_new2 is a Python library that enables simple and fast keyword extraction | ||
| from any text. As the name implies, this library works on the RAKE(Rapid | ||
| Automatic Keyword Extraction) algorithm. | ||
|
|
||
| It tries to determine the key phrases in a text by calculating the | ||
| co-occurrences of every word in a key phrase and also its frequency in the | ||
| entire text. | ||
|
|
||
|  | ||
|
|
||
| ## New in version 1.0.5 | ||
|
|
||
| 1. Handles repetitive keywords/key-phrases | ||
|
|
||
| 2. Handles consecutive punctuations. | ||
|
|
||
| 3. Handles HTML tags in text : The user is allowed an option to choose if they | ||
| want to keep HTML tags as keywords too. | ||
|
|
||
|  | ||
|
|
||
| ## Installation | ||
|
|
||
| Use the package manager [pip](https://pip.pypa.io/en/stable/) to install | ||
| rake_new2. | ||
|
|
||
| ```bash | ||
| pip install rake_new2 | ||
| ``` | ||
|
|
||
| ## Quick Start | ||
|
|
||
| ```python | ||
| from rake_new2 import Rake | ||
|
|
||
| text = "Red apples are good in taste." | ||
| text2 = "<h1> Hello world !</h1>" | ||
| rk,rk_new1,rk_new2 = Rake(),Rake(keep_html_tags=True),Rake(keep_html_tags=False) | ||
|
|
||
| # Case 1 | ||
| # Initialize | ||
| rk.get_keywords_from_raw_text(text) | ||
| kw_s = rk.get_keywords_with_scores() | ||
| # Returns keywords with degree scores : {(1.0, 'taste'), (1.0, 'good'), (4.0, 'red apples')} | ||
| kw = rk.get_ranked_keywords() | ||
| # Returns keywords only : ['red apples', 'taste', 'good'] | ||
| f = rk.get_word_freq() | ||
| # Returns word frequencies as a Counter object : {'red': 1, 'apples': 1, 'good': 1, 'taste': 1} | ||
| deg = rk.get_kw_degree() | ||
| # Returns word degrees as defaultdict object : {'red': 2.0, 'apples': 2.0, 'good': 1.0, 'taste': 1.0} | ||
|
|
||
| # Case 2 : Sample case for testing the 'keep_html_tags' parameter. Default = False | ||
| print("\nORIGINAL TEXT : {}".format(text)) | ||
| # Sub Case 1 : Keeping the HTMLtags | ||
| rk_new1.get_keywords_from_raw_text(text2) | ||
| kw_s1 = rk_new1.get_keywords_with_scores() | ||
| kw1 = rk_new1.get_ranked_keywords() | ||
| print("Keeping the tags : ",kw1) | ||
|
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||
| # Sub Case 2 : Eliminating the HTML tags | ||
| rk_new2.get_keywords_from_raw_text(text2) | ||
| kw_s2 = rk_new2.get_keywords_with_scores() | ||
| kw2 = rk_new2.get_ranked_keywords() | ||
| print("Eliminating the tags : ",kw2) | ||
|
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| '''OUTPUT >> | ||
| ORIGINAL TEXT : <h1> Hello world !</h1> | ||
| Keeping the tags : {'h1', 'hello'} | ||
| Eliminating the tags : {'hello world'} | ||
| ''' | ||
| ``` | ||
|
|
||
| ## Debugging | ||
|
|
||
| You might come across a stopwords error. | ||
|
|
||
| It implies that you do not have the stopwords corpus downloaded from NLTK. | ||
|
|
||
| To download it, use the command below. | ||
|
|
||
| ```python | ||
| python -c "import nltk; nltk.download('stopwords')" | ||
| ``` | ||
|
|
||
| ## Contributing | ||
|
|
||
| Pull requests are welcome. For major changes, please open an issue first to | ||
| discuss what you would like to change. | ||
|
|
||
| Please make sure to update tests as appropriate. | ||
|
|
||
| ## License | ||
|
|
||
| [MIT](https://choosealicense.com/licenses/mit/) | ||
|
|
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| ### Beta Tester | ||
|
|
||
| Sankha Subhra Mondal |