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Overloadpy/Amharic_Sentiment_Classification-engine

Domain:

natural language processing

Record type:

software
Creator:
Ove
Host:
# πŸ‡ͺπŸ‡Ή Amharic Sentiment Intelligence Studio & CLI Engine *A High-Performance, Local, CPU-Optimized Ethiopic NLP Multi-Polarity Classification Platform.* --- -41cd52.svg) -brightgreen.svg) -success.svg) --- ## πŸ‘¨β€πŸ’» Project Leadership & Authorship - **Lead Engineer & Author:** **Yordanos Andargachew** - **Contact & Inquiries:** `+251 952 190 305` (`0952190305`) - **Research Domain:** Low-Resource Semitic NLP, Ethiopic Affective Computing, and Production ML Systems. --- ## 1. System Overview & Core Capabilities **`amh-synth`** is a production-grade NLP platform engineered specifically for **Amharic (αŠ αˆ›αˆ­αŠ›)** sentiment analysis, customer intelligence, and affective computing. Designed from first principles to overcome the linguistic and computational bottlenecks of Semitic languages, it delivers real-time inference on local standard CPUs without cloud dependencies, machine translation layers, or synthetic keyword shortcuts. ``` β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ amh-synth Production Pipeline β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ [ 1. Raw Amharic Text ] ───► "αˆ²αˆ΅α‰°αˆ›α‰½αˆ αˆαˆαŒŠα‹œ αŠ α‹­αˆ°αˆ«αˆα‘ α‰ αŒ£αˆ αŠ αˆ³α‹αˆͺ αŠα‹!" β”‚ [ 2. O(N) Normalizer ] ───► Homophone Unification (ሐ/αŠ€->αˆ€, ሠ->ሰ, ዐ->አ, ፀ->ጸ) Punctuation Transliteration (ፑ, ፒ, ፣ -> ASCII) Elongation Collapse (α‰ αŒ£αˆαˆαˆ -> α‰ αŒ£αˆ) β”‚ [ 3. Clause Splitting ] ───► Syntactic & Discourse Boundary Segmentation β”‚ [ 4. AfriBERTa Engine ] ───► XLMRobertaForSequenceClassification (Torch 4 Threads) Raw Probabilities: P(pos), P(neg), P(neu) β”‚ [ 5. Dual-Axis Math ] ───► Decoupled Calibration: Positive / Negative / Neutral / Mixed β”‚ [ 6. Dual Interfaces ] ───► πŸ–₯️ Desktop Studio GUI (PySide6 / Dark-Light Mode) ⌨️ Terminal CLI Harness (Typer + Rich Live REPL) ``` ### Key Engineering Features: 1. **4-Class Continuous Sentiment Classification:** Maps input text into **`Positive` (αŠ α‹ŽαŠ•α‰³α‹Š)**, **`Negative` (αŠ αˆ‰α‰³α‹Š)**, **` …