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With this app, you can easily extract tweets, analyze sentiment, and visualize trends and patterns in your data. Explore and discover new insights about your Twitter activity and the conversations that matter to you." This description highlights the main features and benefits of the app, and emphasizes its ability to help the user extract and anal

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Tweet Analysis App

This app is built using Python, NLTK, and Streamlit. It allows users to input a Twitter
handle and analyze the sentiment of their recent tweets.

Getting Started

To use this app, follow these steps:

  1. Make sure you have Python and pip installed on your machine. You can check if you have these installed by running the following commands in your terminal:
python --version
pip --version

If you do not have Python or pip installed, you can install them by following the instructions here

  1. Clone this repository and navigate to the root directory of the project.
git clone https://github.com/Ag994/tweets-analysis-app.git
cd tweet-analysis-app
  1. Install the required dependencies.
pip install -r requirements.txt

This will install the nltk and streamlit libraries, which are needed to run the app.

  1. Launch the app by running the following command from the root directory of the project:
streamlit run app.py

This will open the app in your default web browser. From there, you can enter a Twitter handle and click the "Analyze" button to see the sentiment analysis of the user's recent tweets.

Sentiment Analysis

The sentiment analysis is performed using NLTK's built-in sentiment analysis tool. It assigns a polarity score is a value between -1 (very negative) and 1 (very positive), and the subjectivity score is a value between 0 (very objective) and 1 (very subjective). to each tweet based on the words used, with positive words increasing the polarity score and negative words decreasing it.

Future Work

There are many ways that this app could be improved and expanded upon. Some ideas for future work include:

  • Adding more advanced sentiment analysis techniques, such as using machine learning models to classify tweets.
  • Adding additional analysis options, such as word frequency or topic modeling.
  • Implementing user authentication and the ability to save and compare analyses for different users.

Need Help?

If you have any issues with these instructions or encounter any errors while trying to run the app, please don't hesitate to reach out for help. I'm here to assist you in any way I can.

I hope this helps! Let me know if you have any further questions.

About

With this app, you can easily extract tweets, analyze sentiment, and visualize trends and patterns in your data. Explore and discover new insights about your Twitter activity and the conversations that matter to you." This description highlights the main features and benefits of the app, and emphasizes its ability to help the user extract and anal

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