Twitter Sentiment Analysis

The "Twitter Sentiment Analysis using NLP and Machine Learning" aims to harness the power of Natural Language Processing (NLP) and advanced machine learning techniques to accurately predict the sentiments of individuals expressed through Twitter handles or hashtags, while categorizing these sentiments as positive or negative.
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Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
   • Installation
3. Usage
4. Contributing
5. License
6. Contact

About The Project

The core objective of this project is to build a sophisticated sentiment analysis model that can effectively classify tweets as either positive or negative. By training the model on a diverse and comprehensive dataset of labeled tweets, the system will learn to identify linguistic patterns, contextual cues, and emotional expressions that signify positive or negative sentiments.

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Built With

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Getting Started

You can run this project local machine. This project is built on Ubuntu.

Prerequisites

Before running this project on locl machine, you need to create project on the developer.twitter.com and generate the consumer keys, and access token required for the project to get the data from the twitter using API's.

• Twitter Developer URL

Installation

1. Clone the repo

   git clone https://github.com/sourabhdeshmukh/twitter_sentiment_analysis.git
   

2. Setup your python virtual environment

   python3 -m venv <env_name>

3. Activate your virtual environment

   source <env_name>/bin/activate

4. Copy the consumer and access keys in streamlit file and store it inside ~/.streamlit directory

5. Install the python dependecies inside your virtual environment

   pip install -r requirements.txt

6. Run the project using below command.

   streamlit run app.py

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Usage

1. Business Insights: This project's outcomes can be employed by businesses for brand monitoring, understanding customer feedback, and adjusting marketing strategies based on the sentiment trends.

2. Social Listening: Governments and organizations can utilize the sentiment analysis to gauge public opinion on social and political issues.

3. Event Tracking: Tracking sentiments related to specific events or campaigns can help organizations understand the overall impact and success of their initiatives.

4. User Experience Enhancement: Online platforms can utilize sentiment analysis to identify and address user concerns promptly, enhancing user satisfaction.

5. Market Research: Sentiment analysis can serve as a cost-effective method for conducting market research and gathering insights into consumer preferences.

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Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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License

Distributed under the MIT License. See LICENSE.txt for more information.

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Contact

Your Name - @100rabhdeshmukh - sourabh[dot]deshmukh[dot]988[at]gmail[dot]com

Project Link: https://github.com/sourabhdeshmukh/twitter_sentiment_analysis

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