Data Signal Sentiment Transformer (v1.0)
Overview
This model is a fine-tuned BERT-base architecture designed to extract the Data Signal (তথ্য সংকেত) of human emotion from unstructured text. In our framework, the "Data Signal" represents the core semantic sentiment isolated from linguistic noise. It is optimized for high-accuracy classification across social media, product reviews, and customer feedback datasets.
Model Architecture
The model utilizes the standard BERT-base-uncased backbone with an added classification head:
- Encoder: 12-layer, 768-hidden, 12-heads, 110M parameters.
- Input: Tokenized text sequences ($max_length=512$).
- Output: Softmax distribution over three classes (Negative, Neutral, Positive).
The optimization objective uses the standard Cross-Entropy Loss:
$$\mathcal{L} = -\sum_{i=1}^{C} y_i \log(\hat{y}_i)$$
Intended Use
- Market Sentiment Analysis: Monitoring the emotional "Data Signal" in real-time financial news.
- Brand Reputation: Analyzing customer feedback to identify shifts in public perception.
- Content Moderation: Filtering toxic interactions by identifying strong negative signals.
Limitations
- Sarcasm Detection: Like most transformer-based classifiers, this model may struggle with heavy irony or context-dependent sarcasm.
- Domain Specificity: While robust, the "Data Signal" extraction is most accurate on general English prose and may require further fine-tuning for specialized legal or medical jargon.
- Context Window: Limited to 512 tokens; longer documents will be truncated.
