customer_feedback_sentiment_bert
Overview
This model is a fine-tuned BERT (Bidirectional Encoder Representations from Transformers) model designed to categorize customer feedback into three distinct sentiment classes: Negative, Neutral, and Positive. It is optimized for short-to-medium length text such as product reviews, survey responses, and social media mentions.
Model Architecture
The model utilizes the BERT-Base-Uncased backbone.
- Layers: 12 Transformer blocks
- Attention Heads: 12
- Hidden Size: 768
- Classification Head: A linear layer on top of the
[CLS] token output, followed by a softmax function to produce class probabilities.
Intended Use
- E-commerce: Automating the analysis of product reviews to identify common pain points.
- Customer Support: Prioritizing tickets based on the urgency/frustration detected in user messages.
- Market Research: Aggregating sentiment trends across different platforms in real-time.
Limitations
- Language: This specific instance is trained only on English text.
- Context Length: Limited to 512 tokens; longer documents will be truncated, potentially losing critical sentiment cues at the end of the text.
- Sarcasm: Like most NLP models, it may struggle with highly sarcastic or nuanced figurative language.
