This project is focused on analyzing the sentiment of stock news headlines and predicting whether the stock price will rise or fall based on the sentiment analysis results. The project utilizes machine learning techniques for natural language processing and classification.
The dataset used in this project consists of news headlines and their corresponding stock prices for a specific time period. The dataset has been collected from various sources and preprocessed for use in the project. The dataset is split into training and testing sets for model training and evaluation.
The project follows the following methodology:
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Data preprocessing: The news headlines are preprocessed to remove noise and unnecessary information, and then transformed into numerical features using techniques such as bag-of-words, TF-IDF, or word embeddings.
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Sentiment analysis: The preprocessed news headlines are analyzed for sentiment using machine learning techniques such as Naive Bayes, Support Vector Machines, or Recurrent Neural Networks.
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Stock price prediction: The sentiment analysis results are used to predict whether the stock price will rise or fall using machine learning techniques such as Logistic Regression, Decision Trees, or Random Forests.
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Model evaluation: The performance of the model is evaluated on the testing set using metrics such as accuracy, precision, recall, and F1-score.
The following technologies have been used in this project:
- Python 3
- Scikit-learn
- Natural Language Toolkit (NLTK)
- Pandas
- Matplotlib
- Jupyter Notebook
To run this project, follow these steps:
- Clone the repository to your local machine.
- Install the required packages using
pip install -r requirements.txt. - Open the Jupyter Notebook file
stock_sentiment_analysis.ipynb. - Run the cells in the notebook in order.