My basic Inspiration was to learn something new in my first Hackathon ever and implement something that is Simple Yet Cool!! After attending a few workshops at HackPrinceton, I was extremely intrigued by the workshop held by Professor Keane on Wolfram Language. It seemed almost addictive and like a game to me. So I wanted to build something simple yet cool out of it.
What it does
It analyzes the sentiment of the latest tweet of any valid twitter user.
How I built it
I used Wolfram language to build the simple Sentiment Analyser. I connected to the Twitter service and authorized the Wolfram app to analyze the profiles. I then deployed a URL to the cloud that had an active form. In the form, I took any valid twitter username as input. I used this username to check in to that profile and analyze the sentiment of the latest tweet as Positive, Negative or Neutral.
Challenges I ran into
My biggest challenge at HackPrinceton was when I came to know really late that my Teammates are not at all arriving. But, I chose to persevere, learn something new and build something up and running. Thanks to Professor Keane, who mentored me so nicely and I started working on two ideas simultaneously. One was a music genre classifier using audio input, which did not go off well at the last moment due to my RAM issue and the other one was this SimpleClassifier. I just continued to strive along and ended up uploading something up and running even without a Team. Also, I am taking up the music classifier as a longterm project!
Accomplishments that I'm proud of
Although most teams had 3-4 people, I could persevere and ended up making something new using a language I have never used before.
What I learned
I learned really cool information and techniques about the Wolfram Language. I also learned to keep on persevering when nothing seems to go right in the code.
What's next for Sentiment Analysis Of Last Tweet Of A Valid Twitter ID
There are many future prospects: 1) Parse through the entire tweeter history of people and find out what kind of posts they generally tweet. 2) To Classify tweets in terms of other parameters that Positive, Negative or Neutral.Say for example,Politics,Sports,Page3 3)Clustering the twitter IDs into various clusters based on the type of tweets they post.