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AI/ML Mini Project Source Code + Docs + PPT

Sentiment Analysis of Tweets Project

A Sentiment Analysis of Tweets project in Python that classifies tweets as positive, negative or neutral using NLP.

  • Python
  • TensorFlow/Scikit-learn
  • MySQL
  • Remote setup included
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See the project running first, then pay.

Sentiment Analysis of Tweets - Social media image
Image: U.S. Embassy, Jakarta from Jakarta, Indonesia / Public domain via Wikimedia Commons

What is the Sentiment Analysis of Tweets?

The Sentiment Analysis of Tweets project finds out how people feel about a topic. Tweets are cleaned by removing links, mentions and emojis, and then classified as positive, negative or neutral.

Both classic machine learning and deep learning models are trained and compared. Users enter a keyword or text, and charts show the sentiment breakdown. It is built with Python, NLTK, Scikit-learn and Flask.

Best for: BE / B.Tech (CSE, IT, AI & DS), MCA, MSc and BSc Computer Science students. Good as a mini project for your semester submission.

Key Features

Main things you can do with the Sentiment Analysis of Tweets.

  • Tweet dataset
  • Cleaning links, mentions and hashtags
  • Tokenising and lemmatising
  • TF-IDF and embeddings
  • Logistic Regression and LSTM
  • Positive, negative, neutral
  • Sentiment pie charts
  • Word clouds

Project Modules

The project is divided into these modules. Each one has its own screens and tasks.

1Data

  • Collect
  • Clean
  • Label
  • Split

2Model

  • Features
  • Train
  • Evaluate
  • Save

3Web App

  • Enter text
  • Keyword analysis
  • Charts
  • Results

4Analysis

  • Accuracy
  • Word clouds
  • Trends
  • Reports

What You Get

Everything you need to submit, run and explain the project.

Complete Source Code

Full, working source code with clean folder structure and helpful comments. No locked or hidden files.

Database File

Ready-to-use MySQL database with tables and sample data, so the project runs on day one.

Project Documentation

Full project report in Word and PDF: abstract, SRS, system architecture, dataset details, algorithm explanation, accuracy charts and confusion matrix, screenshots, testing and conclusion.

PPT Presentation

A 15–20 slide presentation for your seminar, review or final viva. Easy to edit with your name and college.

Remote Project Setup

We connect to your laptop with AnyDesk or Google Meet, install everything and run the project for you.

Project Explanation

A simple walkthrough of the code and flow, plus common viva questions, so you can explain it with confidence.

Project Documentation includes

  1. Certificate, acknowledgement and abstract
  2. Introduction, problem statement and objectives
  3. Existing system vs proposed system
  4. Software Requirement Specification (SRS)
  5. System design: system architecture, dataset details, algorithm explanation, accuracy charts and confusion matrix
  6. Module description and screenshots
  7. Testing and test cases
  8. Conclusion, future scope and references

PPT Presentation slides

  1. Title and team details
  2. Introduction and problem statement
  3. Objectives
  4. Existing vs proposed system
  5. System architecture
  6. Modules
  7. Technology used
  8. Screenshots / demo
  9. Advantages and future scope
  10. Conclusion

Tech Stack

PartTechnologyUsed for
LanguagePython 3Model training and app logic
ML LibrariesTensorFlow/Scikit-learnTraining and prediction
DataNumPy, Pandas, MatplotlibCleaning and charts
Web UIFlask / StreamlitSimple page to test the model
StorageMySQLSaves users, inputs and results

Software and hardware requirements

  • Laptop with 8 GB RAM (GPU is optional, not required)
  • Python 3.9 – 3.11 and pip
  • VS Code or Jupyter Notebook
  • Libraries from requirements.txt (we include it)

How the Sentiment Analysis of Tweets Works

1

Dataset

A public dataset is loaded, cleaned and split into train and test parts.

2

Training

The model learns patterns from the training data.

3

Testing

Accuracy, precision and recall are checked on test data.

4

Prediction

Users give new input in the web page and get the result instantly.

Remote Project Setup Guidance

Not sure how to run it? We set up the project on your laptop over AnyDesk or Google Meet. No need to visit us.

  1. 1
    Share your details
    Tell us your laptop type (Windows / Mac) and a time that suits you.
  2. 2
    Install AnyDesk
    Install free AnyDesk (or join Google Meet) and share the access code.
  3. 3
    We set it up
    We install the required software, import the database and configure the machine learning project.
  4. 4
    Test together
    We run the project in front of you and check every main feature.
  5. 5
    Understand it
    We explain the code, flow and database so you are ready for your viva.

  1. Install Python 3 and tick Add Python to PATH during setup.
  2. Open the project folder in VS Code and create a virtual environment: python -m venv venv.
  3. Install the libraries: pip install -r requirements.txt.
  4. Run the notebook or training script once to train the model (a trained model is also included).
  5. Start the web app with python app.py and open localhost:5000 in your browser.

Sentiment Analysis of Tweets Images

Sentiment Analysis of Tweets - Growing Social Media Influence on Digital Marketing
Growing Social Media Influence on Digital Marketing Image: mkhmarketing / CC BY 2.0 via Wikimedia Commons
Sentiment Analysis of Tweets - Kids social media
Kids social media Image: Stock Catalog / CC BY 2.0 via Wikimedia Commons

What You Will Learn

Data cleaning and feature engineering Training and comparing ML / DL models Measuring accuracy, precision, recall and F1-score Saving a model and using it in a Flask app Explaining your results in the viva

Frequently Asked Questions

Yes. Sentiment Analysis of Tweets is a practical mini project that solves a real problem. It is suitable for BE / B.Tech (CSE, IT, AI & DS), MCA, MSc and BSc Computer Science students. It is easy to explain in a viva and easy to extend with your own ideas.

This machine learning project is built with Python, TensorFlow/Scikit-learn, MySQL. The full technology list is in the "Tech Stack" section above.

Yes. You get the complete source code and the MySQL database file with sample data. Nothing is locked or hidden.

Yes. You get a full project report (Word and PDF) with diagrams and screenshots, and a ready PPT presentation for your review or viva.

Yes. We offer remote setup using AnyDesk or Google Meet. We install the software, run the project and explain how it works.

Yes. Small changes such as your college name, colours or extra fields can be done. Bigger custom features can be discussed on call or WhatsApp.

We follow a simple rule: you see the project running first, then you make the payment. You can pay by UPI or bank transfer.

Project Details

Available
  • ProjectSentiment Analysis of Tweets
  • TypeMini Project
  • CategoryAI/ML
  • LanguagePython
  • FrameworkTensorFlow/Scikit-learn
  • DatabaseMySQL
  • DocumentationReport + PPT
  • SetupRemote help
  • DeliverySource Code + Documentation
Chat on WhatsApp
Get the project first, then pay. No advance needed.

Need help? Call or WhatsApp

+91 90670 53826 +91 91451 51367

Available every day, 9 AM – 9 PM IST.

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