NLP-FinHeadlines-MoodTracker is an NLP project that performs sentiment analysis on financial news headlines. It aims to predict the sentiment (positive, negative, or neutral) associated with the news headlines and track the overall mood in the financial market. The project utilizes a combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) layers for sentiment classification.

The project includes the following components:

• Data preprocessing: The financial news headlines are preprocessed to remove noise, tokenize the text, and perform other necessary cleaning steps.
• Word embedding: The project uses pre-trained word embeddings to represent words as dense vectors and capture semantic relationships.
• Sentiment analysis model: The model consists of an embedding layer, 1D convolution, max pooling, bidirectional LSTM, dropout, and dense layer for sentiment classification.
• Training and evaluation: The model is trained on a labeled dataset of financial news headlines and evaluated using appropriate metrics.
• Mood tracking: The model's predictions are used to track the overall sentiment or mood in the financial market.

To get started with the project, follow these steps:

1. Clone the repository: git clone https://github.com/YourUsername/NLP-FinHeadlines-MoodTracker.git

2. Install the required dependencies.

3. Preprocess the data and train the sentiment analysis model using the provided notebook or scripts.

4. Evaluate the model's performance and make any necessary adjustments.

5. Use the trained model to analyze and track the sentiment of financial news headlines.

Contributions to the project are welcome! If you'd like to contribute, please follow these steps:

1. Fork the repository on GitHub.

2. Create a new branch from the 'main' branch to work on your changes.

3. Make your modifications and commit your changes.

4. Push your changes to your forked repository.

5. Open a pull request on the main repository to submit your changes for review.

Please ensure that your contributions align with the project's coding style and guidelines.

This project is licensed under the MIT License. See the LICENSE file for more information.

If you have any questions or suggestions, feel free to contact me at [email protected] .