OpinionAI is a machine learning project designed to predict the sentiment (positive, neutral, or negative) of food product reviews. This system aims to provide a reliable method for understanding consumer feedback using both rule-based and transformer-based approaches.
- Programming Language: Python
- Libraries: NLTK, spaCy, Hugging Face Transformers, RoBERTa, VADER
- Data Cleaning: Utilized NLTK and spaCy for data preprocessing to ensure high-quality input for sentiment analysis.
- Sentiment Analysis Approaches:
- VADER: Implemented for initial rule-based sentiment scoring, providing a straightforward approach for sentiment classification.
- RoBERTa: Leveraged the Hugging Face Transformers library for transformer-based sentiment analysis, capturing nuanced sentiment distinctions with higher precision.
- Comparative Analysis: Conducted a detailed comparison between VADER and RoBERTa, revealing that while both models performed well for positive and negative reviews, RoBERTa excelled in detecting neutral sentiments with greater accuracy.
The project highlighted the advantages of using advanced transformer models like RoBERTa for handling complex sentiment distinctions, making it a valuable tool for consumer feedback analysis. The comparative insights provided a clear understanding of each model’s strengths and limitations.
- The effectiveness of using different sentiment analysis approaches for diverse data scenarios.
- How transformer models can capture nuanced sentiments compared to traditional rule-based methods.
- Importance of thorough data preprocessing to achieve reliable analysis outcomes.
Visit the GitHub Repository for full access to the code, data cleaning steps, and analysis scripts.
- Extending the analysis to include more domain-specific training data for even greater accuracy.
- Integrating additional transformer models to explore performance variations.
For questions, collaboration, or feedback, reach out via LinkedIn or GitHub.