UPDATE: The github repo for twitter sentiment analyzer now contains
updated get_twitter_data.py file compatible with Twitter API v1.1.
It can be tested by placing appropriate oauth credentials in config.json
and running test_twitter_data.py.
You can create a new twitter app at https://dev.twitter.com/apps to fetch necessary oauth credentials.
Hi all, It's been almost a year since I last wrote a technical post. A lot of changes have occurred in my life since then,
from a Frontend engineer at Yahoo!, I've transformed into a full-time graduate student at UNC-Chapel Hill who is moving to
Redmond to do an internship at Microsoft this summer. In my spring semester, I took Data Mining course for which I had to
complete a project as part of the course. After exploring various ideas, I finalized on building a Twitter Sentiment Analyzer.
This project aimed to extract tweets about a particular topic from twitter (recency = 1-7 days) and analyze the opinion of
tweeples (people who use twitter.com) on this topic as positive, negative or neutral. In this post, I will explain you how
you can build such a sentiment analyzer. I will try to explain the concepts without making it sound too technical,
but a good knowledge of machine learning classifiers really helps.