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--- |
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language: en |
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license: apache-2.0 |
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tags: |
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- nlp |
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- sentiment-analysis |
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- bert |
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- classification |
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metrics: |
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- accuracy |
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- f1 |
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--- |
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# customer_feedback_sentiment_bert |
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## Overview |
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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. |
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## Model Architecture |
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The model utilizes the **BERT-Base-Uncased** backbone. |
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- **Layers**: 12 Transformer blocks |
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- **Attention Heads**: 12 |
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- **Hidden Size**: 768 |
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- **Classification Head**: A linear layer on top of the `[CLS]` token output, followed by a softmax function to produce class probabilities. |
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## Intended Use |
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- **E-commerce**: Automating the analysis of product reviews to identify common pain points. |
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- **Customer Support**: Prioritizing tickets based on the urgency/frustration detected in user messages. |
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- **Market Research**: Aggregating sentiment trends across different platforms in real-time. |
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## Limitations |
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- **Language**: This specific instance is trained only on English text. |
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- **Context Length**: Limited to 512 tokens; longer documents will be truncated, potentially losing critical sentiment cues at the end of the text. |
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- **Sarcasm**: Like most NLP models, it may struggle with highly sarcastic or nuanced figurative language. |
