Project Description
This repository is a submission for the Sentiment Analysis project in IDCamp 2024. The project focuses on sentiment analysis using various techniques and tools available in Jupyter Notebook.
Programming Language
- Jupyter Notebook
Key Features
- Data Analysis: Collecting and cleaning data from various sources.
- Text Preprocessing: Techniques such as tokenization, stemming, and lemmatization.
- Machine Learning Models: Implementation of various machine learning models for sentiment analysis.
- Model Evaluation: Evaluating model performance using metrics such as accuracy, precision, and recall.
- Data Visualization: Visualizing analysis results with graphs and charts for better interpretation.
Technologies Used
- Jupyter Notebook: For code development and documentation.
- Pandas: For data manipulation and analysis.
- Scikit-learn: For implementing machine learning models.
- Matplotlib and Seaborn: For data visualization.
- TensorFlow: For deep learning model implementation.
Project Highlights
- Reproducibility: All steps and results can be reproduced using Jupyter Notebook, ensuring transparency and ease of use.
- Popular Libraries: Utilizes popular libraries such as Pandas and Scikit-learn to ensure performance and reliability.
Repository Link
GitHub - alrescha79-cmd/analisis-sentimen