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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