Sentiment analysis using machine learning

[Ananya-vastare]

Pull Request for PyVerse 💡

Requesting to submit a pull request to the PyVerse repository.

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Issue Title

Related Issue:
#452 Sentiment analysis

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Info about the Related Issue

What's the goal of the project?
To automatically identify and classify human emotions expressed in text.

Aim of the project:

1. Accurately detect and categorize sentiment and specific emotions.
2. Identify trends and patterns in emotions over time or demographics.
3. Provide real-time feedback for actionable insights.
4. Visualize results in an easy-to-understand format for decision-making.

• I have described the aim of the project.

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Your Information

Name:
Ananya Ravikiran Vastare

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GitHub ID:
@Ananya-vastare

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Email ID:
[email protected]

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Program:
GSSOC extd, Hacktoberfest

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Issue Reference

Closes:
Closes: #452

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Description of Changes

Add-ons or Modifications:
Implemented a sentiment analysis model to classify text data by identifying emotions. Added real-time processing for immediate insights and designed a visual dashboard to display sentiment trends. Enhancements include:

• Fine-tuning the model for higher accuracy across various emotions.
• Integrating user feedback for adaptive learning.
• Optimizing the interface for seamless interaction.

These changes aim to create a more responsive, accurate, and visually engaging sentiment analysis tool tailored to user needs.

• I have described my changes.

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Type of Change

Select the type of change:

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Code style update (formatting, local variables)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• This change requires a documentation update

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Testing Information

Testing Process:
The changes were tested using a dataset with labeled emotions to evaluate model accuracy across different sentiment classes. Real-time analysis was validated with live input to ensure responsiveness. The dashboard was tested for usability and clarity through user feedback sessions.

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Checklist

Please confirm the following:

• My code follows the guidelines of this project.
• I have performed a self-review of my own code.
• I have commented my code, particularly wherever it was hard to understand.
• I have made corresponding changes to the documentation.
• My changes generate no new warnings.
• I have added elements that prove my fix is effective or that my feature works.
• Any dependent changes have been merged and published in downstream modules.

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[Ananya-vastare]

@ruhi47 @UTSAVS26 please let me know if I need to make any changes