From 348102464d0522d0e9b3a48c005ed11c0c6b748e Mon Sep 17 00:00:00 2001 From: OmarTahoun Date: Sun, 24 Feb 2019 14:44:09 +0800 Subject: [PATCH] Closes #4 --- README.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 851d6cf..ffc3cd2 100644 --- a/README.md +++ b/README.md @@ -1,18 +1,18 @@ -# twitter_sentiment_challenge -Twitter Sentiment Analysis Challenge for Learn Python for Data Science #2 by @Sirajology on Youtube +# twitter-sentiment-challenge +Twitter Sentiment Analysis Challenge for Learn Python for Data Science #2 by [@Sirajology](https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A) on Youtube -##Overview +## Overview -This is the code for the Twitter Sentiment Analyzer challenge for 'Learn Python for Data Science #2' by @Sirajology on [YouTube](https://youtu.be/o_OZdbCzHUA). The code uses the [tweepy](http://www.tweepy.org/) library to access the Twitter API and the [TextBlob](https://textblob.readthedocs.io/en/dev/) library to perform Sentiment Analysis on each Tweet. We'll be able to see how positive or negative each tweet is about whatever topic we choose. +This is the code for the Twitter Sentiment Analyzer challenge for 'Learn Python for Data Science #2' by @Sirajology on [YouTube](https://youtu.be/o_OZdbCzHUA). The code uses the [tweepy](http://www.tweepy.org/) library to access the Twitter API and the [TextBlob](https://textblob.readthedocs.io/en/dev/) library to perform Sentiment Analysis on each Tweet. We'll be able to see how positive or negative each tweet is about whatever topic we choose. -##Dependencies +## Dependencies * tweepy (http://www.tweepy.org/) * textblob (https://textblob.readthedocs.io/en/dev/) Install missing dependencies using [pip](https://pip.pypa.io/en/stable/installing/) -##Usage +## Usage Once you have your dependencies installed via pip, run the script in terminal via @@ -20,10 +20,10 @@ Once you have your dependencies installed via pip, run the script in terminal vi python demo.py ``` -##Challenge +## Challenge Instead of printing out each tweet, save each Tweet to a CSV file with an associated label. The label should be either 'Positive' or 'Negative'. You can define the sentiment polarity threshold yourself, whatever you think constitutes a tweet being positive/negative. Push your code repository to [github](https://help.github.com/articles/set-up-git/) then post it in the comments. I'll give the winner a shoutout a week from now! -##Credits +## Credits This code is 100% Siraj baby.