Title: Improving Sentiment Analysis Accuracy for News Headlines

[bhanushri12]

Is your proposal related to a problem? Please describe.

Currently, the sentiment analysis module in our project analyzes news headlines using basic polarity and subjectivity metrics. To enhance the accuracy and effectiveness of sentiment analysis, especially for predicting stock market trends based on news sentiment, we propose implementing advanced techniques and models.

Add any other context or screenshots about the proposal request here.

Textual Analysis

Functions to get the subjectivity and polarity

def getSubjectivity(text): return TextBlob(text).sentiment.subjectivity

def getPolarity(text): return TextBlob(text).sentiment.polarity

Adding subjectivity and polarity columns

ndf['Subjectivity'] = ndf['News'].apply(getSubjectivity) ndf['Polarity'] = ndf['News'].apply(getPolarity) ndf

[github-actions[bot]]

@bhanushri12

It's great having you contribute to this project

Thanks for opening this Issue 🙌 , Welcome to Project Guidance 💖 We will review everything and get back to you.
Make sure to give a star to this repo before making a fork! Thank you :)

[bhanushri12]

Can I Start working on this?