Tech Stack
Description
This project is an advanced AI application designed to provide robust, multi-language sentiment analysis. The core objective was to build a tool that not only accurately predicts sentiment (positive, negative, neutral) across languages like English and Chinese but also makes the decision-making process transparent and understandable for the user.
The technical foundation relies on state-of-the-art Transformer models from the Hugging Face ecosystem, including RoBERTa and other BERT variants. The backend is engineered for efficiency, featuring an LRU caching system to manage model memory and parallel processing for handling batch requests, ensuring high throughput and stability for over 4000 potential users.
A key differentiator of this platform is its integration of Explainable AI (XAI) techniques. By leveraging SHAP and LIME, the application generates intuitive visualizations that highlight which specific words or tokens most influenced the model's sentiment prediction. This layer of transparency is crucial for building user trust and providing deeper insights beyond a simple classification score.
- Implemented high-accuracy sentiment analysis for multiple languages using Transformer models like RoBERTa.
- Integrated Explainable AI (XAI) frameworks (SHAP & LIME) to visualize and interpret model predictions.
- Developed an interactive and user-friendly interface with Gradio for single, batch, and XAI analysis.
- Engineered a performance-optimized backend with LRU model caching and efficient memory management.
- Enabled comprehensive data interaction with features for history tracking and data export to CSV/JSON formats.
Page Info
Single Analysis Page
User-friendly interface for analyzing single text inputs. Results are displayed with clear visualizations of sentiment probabilities.
Batch Analysis Page
User-friendly interface for processing entire files in batches. Results are displayed with clear visualizations of sentiment probabilities.
Explainable AI (XAI) Dashboard
Interactive dashboard to run SHAP and LIME analyses. It visualizes which words or tokens contribute most to the sentiment prediction, making the AI's decisions transparent.
History & Analytics
A dedicated tab to view the history of all analyses performed. Provides summary statistics and allows users to export the complete history for external use.
