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.