This project leverages machine learning and deep learning techniques to analyze customer reviews, providing insights and sentiment analysis. The implementation includes an STV (Single Transferable Vote) voting method to improve classification results by combining predictions from multiple models.This project blends cutting-edge AI techniques to provide a comprehensive, robust solution for customer sentiment analysis, enabling better business outcomes through informed decision-making.
- Sentiment Analysis: Classifies customer reviews into categories such as positive, negative, and neutral.
- Machine Learning Models: Implements traditional ML algorithms like Random Forest and SVM.
- Deep Learning Models: Utilizes neural networks, such as LSTMs or CNNs, for deeper textual understanding.
- STV Voting: Combines outputs from multiple models to achieve higher accuracy and robustness.
- Data Visualization: Includes graphical representations of insights extracted from customer reviews.
- Clone the repository:
git clone https://github.com/AlinaBaber/Customer-Reviews-Analysis-with-Machine-Learning-Deep-Learning---STV-Voting-Method.git
- Navigate to the project directory:
cd Customer-Reviews-Analysis-with-Machine-Learning-Deep-Learning---STV-Voting-Method- Usage Prepare your dataset by placing it in the data/ directory. Ensure it is in CSV format with appropriate headers. Run the Jupyter Notebook to execute the analysis:
jupyter notebook CustomerReviewsAnalysis_(3).ipynbFollow the notebook instructions to preprocess data, train models, and evaluate results.
This project is designed to analyze and classify customer reviews into sentiment categories such as positive, negative, and neutral. It combines advanced data analysis techniques with state-of-the-art algorithms to derive meaningful insights from textual feedback, helping businesses make data-driven decisions.
- The project starts by cleaning and preparing the customer review data.
- This involves:
- Text cleaning (removal of special characters, stop words, etc.).
- Tokenization.
- Transforming text data into numerical formats like TF-IDF or word embeddings.
- Multiple models are trained to analyze sentiments:
- Machine Learning Models: Algorithms like Random Forest, SVM, and Logistic Regression are used for traditional sentiment classification.
- Deep Learning Models: Neural network architectures such as LSTM (for sequence data) and CNNs (for feature extraction) are applied for more nuanced analysis of customer reviews.
- Instead of relying on a single model, the project integrates predictions from multiple models using the STV voting method.
- This approach ensures that the final sentiment classification is more robust, combining the strengths of various models to mitigate individual weaknesses.
- The performance of individual models and the ensemble voting system is evaluated using metrics like:
- Accuracy
- Precision
- Recall
- F1-Score
- The project generates visual representations of the results, such as:
- Sentiment distribution across the dataset.
- Comparative performance metrics for each model.
- Insights into frequently used words or phrases in customer reviews.
- By leveraging machine learning, deep learning, and ensemble techniques, the project achieves higher accuracy and better reliability than using standalone models.
- This demonstrates the effectiveness of combining traditional and modern AI approaches for text analysis.
- Customer Feedback Analysis: Understand customer sentiments to improve services or products.
- Business Decision Support: Use insights for strategic planning based on customer opinions.
- Marketing Analytics: Identify trends and public sentiment about brands or products.
Contributions are welcome!
This project is licensed under the MIT License.
Alina Baber for the repository and project inspiration. Contributors and open-source libraries utilized in this project.
Let me know if you’d like to add more sections or have additional details to include!