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        "author": "John Keck",
        "author_email": "robertjkeck2@gmail.com",
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            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Topic :: Scientific/Engineering :: Artificial Intelligence"
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        "description": "==========\r\nlikability\r\n==========\r\n\r\nUsing empythy to score likability based on sentiment analysis of recent tweets about a given person\r\n\r\nPurpose\r\n======= \r\n\r\nTo piggyback off of the empythy natural languare classifier package to analyze average sentiment of tweets related to a particular person to calculate a \u2018likability score\u2019 for that person. Useful in tracking sentiment changes across a certain period of time, i.e. the likability score of a celebrity before and after a concert.\r\n\r\nInstructions \r\n============\r\n\r\n- Open terminal. Make sure you have `python3` and `pip` downloaded. \r\n- pip install likability \r\n- Create a csv file with the names of the people you\u2019d like to analyze for likability. Name this file name.csv in the current directory.\r\n- Determine how many recent tweets you\u2019d like to query for each person. This will be used in the script below as `num_tweets`.\r\n- Make sure you have Twitter API keys and access tokens. If you do not, go to [Twitter Apps](https://apps.twitter.com/), create an app, and find the required keys and tokens under Applications Settings -> Consumer Key (API Key) -> manage keys and access tokens.\r\n- Run Python 3 by typing `python` into the terminal. \r\n- Enter script below to run the LikabilityAnalyzer module. \r\n\r\n- ``from likability import LikabilityAnalyzer``\r\n- ``filepath = 'name.csv'``\r\n- ``num_tweets = 100`` \r\n- ``sentimentScore = LikabilityAnalyzer.analyzer(filepath,num_tweets)``\r\n\r\n- When prompted, enter in your Twitter API keys. This will allow likability to access the Twitter API to query the tweets needed to complete the sentiment analysis. \r\n- Wait for script to run to completion. Please note, due to Twitter API Rate Limiting, querying more than 15 names will lead to longer wait times. Please allow 1 minute per name for lists greater than 15 names.\r\n- Upon completion, open the newly created Sentiment.csv in the current directory to access the likability scores for each person.\r\n\r\nPossible Usage\r\n==============\r\n\r\n- Score top fantasy football players to see what the Twittersphere thinks about each player pre-draft\r\n- Instead of names of people, use product names to track customer sentiment in real-time \r\n- Solve the question: who is more likable, Justin Timberlake or Jimmy Fallon",
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        "keywords": "machine learning,data science,NLP,natural language processing,sentiment,sentiment analysis,sentiment prediction,twitter corpus,twitter,tweets corpus,movie reviews corpus,NLTK,automated machine learning",
        "license": "MIT",
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        "name": "lykability",
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        "summary": "Using empythy to score likability based on sentiment analysis of recent tweets about a given person",
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