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        "author": "Michal Pikusa",
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        "description": "# Topic Network - topic modeling with text networks\nThis package introduces a novel method for topic modeling using community detection in complex networks. The algorithms included in the package first create a network of collocated terms, filter out unimportant words based on centrality measures, and then use community detection to reveal the topic groups in the network. \n\nThe methods used are language-agnostic, meaning that you can perform the topic modeling on any text in any language. \n\nIt is an early version, hence there might be performance issues when modeling big corpora. These are to be resolved in upcoming updates.\n\n\n\n## Installation\n\n\nTo install the current version of the package, use:\n\n\n```python\n\npip install topicnetwork\n\n```\n\n\n\n## Use\n\n\nTo find the topics, simply use:\n\n\n\n```python\n\nimport topicnetwork\n\ntopics = topicnetwork.find_topics(list_of_strings)\n\n```\n\n\n\nFor best results, use a text without punctuation and stopwords, and words converted to lowercase. You can perform the cleaning on your English texts with:\n\n\n\n```python\n\ntext = topicnetwork.clean(list_of_strings)\n\n```\n\n\n\n## Notes\n\nPackage written and maintained by Michal Pikusa (pikusa.michal@gmail.com)\n\n",
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