semvec/pypi-packages huggingface dataset:
lykability 1.0.0
Using empythy to score likability based on sentiment analysis of recent tweets about a given person
License: MIT
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

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likability
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Using empythy to score likability based on sentiment analysis of recent tweets about a given person

Purpose
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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 module. 

- ``from likability import LikabilityAnalyzer``
- ``filepath = 'name.csv'``
- ``num_tweets = 100`` 
- ``sentimentScore = LikabilityAnalyzer.analyzer(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
DeepLearningStack 0.0.9
A Python package for creating deep network graphs using Theano
License: MIT
Keywords: deep recurrent neural network reinforcement learning

DeepLearningStack
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A project that helps develop deep learning models using Theano with xml configuration.

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DeepLearningStack allows easy definition of deep neural networks with XML configuration files.
The package contains modules for definition of MLP, convolutional networks, reinforcement learning and recurrent neural networks.