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        "author": "Christoph Fink",
        "author_email": "christoph.fink@helsinki.fi",
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            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
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        "description": "# Python wrapper for the webis Twitter sentiment evaluation ensemble\n\nThis is a Python wrapper around the Java implementation of a Twitter sentiment evaluation framework presented by [Hagen et al. (2015)](http://www.aclweb.org/anthology/S15-2097). It uses [PyJnius](https://github.com/kivy/pyjnius/tree/master/jnius) to call the Java modules to evaluate sentiment.\n\n### Dependencies\n\nThe script is written in Python 3 and depends on the Python modules [PyJnius](https://github.com/kivy/pyjnius/tree/master/jnius), [pandas](https://pandas.pydata.org/) and [emojientities](https://gitlab.com/christoph.fink/python-emoji-range). \n\nOn top of that, a Java Runtime Environment (jre) is required, plus a matching Java Development Kit (jdk). We used Java 8, but other versions might work just as well. [OpenJDK](https://openjdk.java.net/) works fine.\n\nTo install all dependencies on a Debian-based system, run:\n\n```shell\napt-get update -y &&\napt-get install -y python3-dev python3-pip python3-virtualenv cython3 openjdk-8-jdk-headless openjdk-8-jre-headless ca-certificates-java\n```\n\n(There\u2019s an Archlinux AUR package pulling in all dependencies, see further down)\n\n### Installation\n\n- *using `pip` or similar:*\n\n```shell\npip3 install webis\n```\n\n- *OR: manually:*\n\n    - Clone this repository\n\n    ```shell\n    git clone https://gitlab.com/christoph.fink/python-webis.git\n    ```\n\n    - Change to the cloned directory    \n    - Use the Python `setuptools` to install the package:\n\n    ```shell\n    cd python-webis\n    python3 ./setup.py install\n    ```\n\n- *OR: (Arch Linux only) from [AUR](https://aur.archlinux.org/packages/python-webis):*\n\n```shell\n# e.g. using yaourt\nyaourt python-webis\n```\n\n### Usage\n\nImport the `webis` module. On first run, *python-webis* will download and compile the Java backend \u2013 this might take a few minutes.\n\nThen instantiate a `webis.SentimentIdentifier` object and use its `identifySentiment()` function, passing in a list of tuples (`[(tweetId, tweetText),(tweetId, tweetText), \u2026 ]`), a dict (`{tweetId: tweetText, \u2026 }`) or a `pandas.DataFrame` (first column is treated as identifier, second as tweetText). \n\nThe function returns a list of tuples (`[(tweetId, sentiment), \u2026 ]`), a dict (`{tweetId: sentiment, \u2026 }`) or a data frame (first column id, second column sentiment) of rows it successfully identified a sentiment of. The type of the return value matches the argument, with which the function is called. The `tweetId` values will be cast to the type of the first row\u2019s `tweetId`.\n\n```python\nimport webis\n\nsentimentIdentifier = webis.SentimentIdentifier()\n\n# list of tuples\ntweets = [\n    (1, \"What a beautiful morning! There\u2019s nothing better than cycling to work on a sunny day \ud83d\udeb2.\"),\n    (2, \"Argh, I hate it when you find seven (7!) cars blocking the bike lane on a five-mile commute\")\n]\n\ntweets = sentimentIdentifier.identifySentiment(tweets)\n# [(1, \"positive\"), (2, \"negative\")]\n\n# pandas Dataframe\nimport pandas\ntweets = pandas.DataFrame([\n    (1, \"What a beautiful morning! There\u2019s nothing better than cycling to work on a sunny day \ud83d\udeb2.\"),\n    (2, \"Argh, I hate it when you find seven (7!) cars blocking the bike lane on a five-mile commute\")\n])\n\ntweets = sentimentIdentifier.identifySentiment(tweets)\n#   sentiment tweetId\n# 0  positive       1\n# 1  negative       2\n\n# dict\ntweets = {\n    1: \"What a beautiful morning! There\u2019s nothing better than cycling to work on a sunny day \ud83d\udeb2.\",\n    2: \"Argh, I hate it when you find seven (7!) cars blocking the bike lane on a five-mile commute\"\n}\n\ntweets = sentimentIdentifier.identifySentiment(tweets)\n# { 1: \"positive\", 2: \"negative\" }\n\n```\n\n`python-webis` can act as a *context manager*:\n\n```python\nwith webis.SentimentIdentifier() as s:\n    tweets = s.identifySentiment(tweets)\n```\n\n`webis.identifySentiment()` is a short-hand for initialising a `SentimentIdentifier` object and calling its `identifySentiment()` method:\n\n```python\ntweets = webis.identifySentiment(tweets)\n```\n\n\n",
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