Metadata-Version: 2.1
Name: Social-Media-Sentiment-Analysis
Version: 0.1.3
Summary: A Library for webscraping social media platforms (twitter) and using sentiment analysis on them!
Home-page: https://social_media_sentiment_analysis.readthedocs.io/
Author: Raf Muz
Author-email: CyberRaf01@gmail.com
License: MIT
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
Requires-Dist: snscrape
Requires-Dist: flair (>=0.11.1)
Requires-Dist: pandas

# Social Media Sentiment Analysis
A Library for webscraping social media platforms (twitter) and using sentiment analysis on them!

## Installation

	pip install social_media_sentiment_analysis


## Get started
Get Tweets from twitter and apply sentiment analysis on it:

~~~Python
# Import Library's
import pandas
from Social_Media_Sentiment_Analysis import Social_Media
from Social_Media_Sentiment_Analysis import NLP_Classification as Classify

tweets = Social_Media.get_tweets ('BTC', 'lang:"en"', 128)  # Get Tweets
tweets, twitter_score = Classify.twitter_indicator (tweets) # Apply Sentiment Analysis

Social_Media.save_tweets (tweets, 'tweets')                 # Save Tweets
tweets = pandas.read_csv ('tweets.csv')                     # Read Tweets

# Print the Results
print (tweets)
print ('\n{0}'.format (twitter_score))
~~~

Documentaion: https://social-media-sentiment-analysis.readthedocs.io/en/latest/

And that's the end of the Readme, Thanks for Reading!


