Inspiration

Lots of Text data go unused and wasted which contains lots of valuable information.

What it does

Performs Natural Language Processing NLP

How we built it

Python

NLP

Jupyter Environment

Challenges we ran into

Getting a Good Dataset

Cleaning the Dataset as it had lots of non-valuable information.

Building a good ML Model that performs well in the Real-World Text Data

Accomplishments that we're proud of

Overcame all the Challenges that hindered our Progress.😁😁

What we learned

Working on Text-Data using NLTK

What's next for Sentiment Analysis using Python

Making it accessible to everyone helps individual persons or businesses to evaluate the performance of their company or the model and improve as per their convenience. Need to develop a Front-End where the user enters the link or the Actual Text whose Sentiment has to be Analysed.

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