Financial Sentiment Analysis!
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In a project focused on financial sentiment analysis, I embarked on using naive Bayes and SVM models to analyze sentiment in financial data. Here's an overview of the key aspects of the project:
- Exploratory Data Analysis (EDA): I began by conducting EDA to understand the financial dataset, including its structure and key features related to sentiment.
- Data Cleaning: I meticulously cleaned the financial data, addressing issues such as missing values, outliers, and inconsistencies to ensure the integrity of the dataset for analysis.
- Feature Engineering: I identified and engineered relevant features that could potentially impact sentiment analysis, such as keyword extraction and sentiment lexicons.
- Model Selection and Training: I selected the naive Bayes and SVM models for sentiment analysis due to their effectiveness in text classification tasks. I trained these models using the cleaned and engineered features.
- Model Evaluation: I evaluated the performance of the models using metrics such as accuracy, precision, recall, and F1-score to assess their effectiveness in predicting sentiment.
This section should list any major frameworks/libraries used in this project. Here are a few examples.
Welcome to the Maji Ndogo project! This README provides instructions on setting up the project locally.
Before you begin, ensure you have the following installed:
- python: Install Python
- seaborn, matplotlib, pandas: Install using pip.
Follow these steps to get a local copy up and running:
- Clone the repo
git clone https://github.com/Addika1630/Integrated_project.git
- Install packages
pip install seaborn
pip install matplotlib
pip install pandas
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!
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the MIT License. See LICENSE for more information.
Addisu Mulat - @twitter - addisu05mulat@gmail.com
Project Link: https://github.com/your_username/repo_name
I would like to acknowledge the following resources that were helpful in this project:
I also want to thank the open-source community for their contributions and support.