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        "author": "Chuancong Gao",
        "author_email": "chuancong@gmail.com",
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        "description": "[![PyPI version](https://img.shields.io/pypi/v/TagStats.svg)](https://pypi.python.org/pypi/TagStats/)\n[![PyPI pyversions](https://img.shields.io/pypi/pyversions/TagStats.svg)](https://pypi.python.org/pypi/TagStats/)\n[![PyPI license](https://img.shields.io/pypi/l/TagStats.svg)](https://pypi.python.org/pypi/TagStats/)\n\nA concise yet efficient implementation for computing the statistics of each tag's set of key phrases over input lines in Python 3.\nOne of the major applications is for [sentiment analysis](https://en.wikipedia.org/wiki/Sentiment_analysis), where each tag is a sentiment with the respective key phrases describing the sentiment.\n\n# How it Works\n\nA [trie](https://en.wikipedia.org/wiki/Trie) structure is constructed to index all the key phrases. Then each line is matched towards the index to compute the respective statistics.\nThe time complexity is $O(m^2 \\cdot n)$, where $m$ is the maximum number of words in each line and $n$ is the number of lines.\n\n# Installation\n\nThis package is available on PyPI. Just use `pip3 install -U TagStats` to install it.\n\n# Usage\n\nYou can simply call `compute(content, tagDict)`, where `content` is a list of lines and `tagDict` is a dictionary with each tag name as key and the respective set of key phrases as value.\n\n``` python\nfrom tagstats import compute\n\nprint(compute(\n    [\n        \"a b c\",\n        \"b c\",\n        \"a c e\"\n    ],\n    {\n        \"+\": [\"a b\", \"a c\"],\n        \"-\": [\"b c\"]\n    }\n))\n```\n\nThe output is a dictionary with each tag name as key and the respective totaled frequencies for lines as value.\n\n``` python\n{'+': [1, 0, 1], '-': [1, 1, 0]}\n```\n\nYou can change the default tokenizer, by specifying a compiled regex as separator to `tagstats.tagstats.tokenizer`. You can disable the tokenizer to allow matching over word boundaries, by specifying `None`.\n\nYou can pre-build the index by calling `index(tagDict)`, and later reuse it more than once as an optional parameter of `compute(content, tagDict, index)`. \n\n# Tip\n\nI strongly encourage using PyPy instead of CPython to run the script for best performance.",
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