Automatic sentimental analyzer

twitter
Text and comment based

Inspiration

The inspiration for building this came form amazon.com i was just scrolling through amazon and saw that there where various columns from good to bad reviews so thought of making something with that and hackathons are the best place to invent and make progress.

What it does

The automatic sentimental analyzer takes user data from twitter and then compare the tweets and sort them out into positive, negative, and neutral feedback.

How I built it

I used tweepy to do web scraping of the data the used NLP libraries to sort them out according to there polarity and subjectivity according to which I was able to differentiate that the tweet was positive or negative

after that, I saved the model and integrated it with Flask to make an API of the same

later I deployed the API on the google cloud platform and Eureka the project was ready

Challenges I ran into

Web scrapping as twitter does not allow people to perform web scraping that easily so I was forced to go through there policies and then learn about Twweepy. I was also able to learn about NLP that is Natural language processing that was a bit new thing for me

What I learned

I learned about how to deploy models on Google cloud platforms and how to make a flask API on the same. I also learned about NLP that is Natural language processing that was a bit new to me