Metadata-Version: 1.1
Name: lykability
Version: 1.0.0
Summary: Using empythy to score likability based on sentiment analysis of recent tweets about a given person
Home-page: https://github.com/robertjkeck2/lykability
Author: John Keck
Author-email: robertjkeck2@gmail.com
License: MIT
Description: # likability
        > Using empythy to score likability based on sentiment analysis of recent tweets about a given person
        
        ## Purpose
        To piggyback off of the empythy natural languare classifier package to analyze average sentiment of tweets related to a particular person to calculate a 'likability score' 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.
        
        
        ## Instructions
        - Open terminal.  Make sure you have ```python3``` and ```pip``` downloaded.
        - ```pip install likability```
        - Create a csv file with the names of the people you'd like to analyze for likability.  Name this file name.csv in the current directory.
        - Determine how many recent tweets you'd like to query for each person.  This will be used in the script below as ```num_tweets```.
        - 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.
        - Run Python 3 by typing ```python``` into the terminal.
        - Enter script below to run the LikabilityAnalyzer.
        ```
        from likability import LikabilityAnalyzer
        filepath = name.csv
        num_tweets = 100
        sentimentScore = LikabilityAnalyzer(filepath,num_tweets)
        ```
        - 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.  
        - 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.
        - Upon completion, open the newly created Sentiment.csv in the current directory to access the likability scores for each person.
        
        
        ## Possible Usage
        - Score top fantasy football players to see what the Twittersphere thinks about each player pre-draft
        - Instead of names of people, use product names to track customer sentiment in real-time
        - Solve the question: who is more likable, Justin Timberlake or Jimmy Fallon
        
        
        
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
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
