Sentiment Analysis

Sentiment analysis is a text classification task where a model assigns a label (e.g., positive, negative, neutral) to a piece of text based on the expressed emotion or opinion. Operators encounter it when using local models like Llama 3.2 or BERT-based classifiers to process user feedback, social media posts, or support tickets. The output is typically a label and a confidence score. Running sentiment models locally avoids sending sensitive text to external APIs and allows custom fine-tuning on domain-specific data.

Sentiment analysis is typically implemented as a sequence classification task in transformer models. A pre-trained model (e.g., DistilBERT, RoBERTa) is fine-tuned on labeled datasets like IMDb or SST-2. The model's final hidden state for the [CLS] token is passed through a linear layer to produce logits over sentiment classes. Operators can run these models locally using Hugging Face Transformers, llama.cpp (with GGUF models that include a classification head), or vLLM. Key considerations: model size vs. accuracy trade-offs (e.g., DistilBERT at ~250 MB vs. RoBERTa-large at ~1.5 GB), inference latency (typically 10-50 ms on a GPU), and the need for GPU VRAM for batch processing. Quantization (e.g., Q8) can reduce model size with minimal accuracy loss.

An operator runs a local sentiment classifier on customer reviews using Hugging Face Transformers. They load distilbert-base-uncased-finetuned-sst-2-english (about 250 MB) on an RTX 3060 12 GB. For a batch of 32 reviews, inference takes 30 ms. The model outputs labels 'POSITIVE' or 'NEGATIVE' with confidence scores. If VRAM is tight, they can use the ONNX runtime or quantize the model to Q8 (130 MB) with negligible accuracy drop.

In a local AI pipeline, an operator uses pipeline('sentiment-analysis') from Hugging Face Transformers to classify incoming support tickets. They run the pipeline on a CPU with a quantized model to save VRAM for other tasks. The output is a list of dicts: [{'label': 'POSITIVE', 'score': 0.98}, ...]. They log the results to a CSV for monthly sentiment trend analysis. If throughput is critical, they switch to vLLM with a small BERT model to handle 1000+ requests per second on a single GPU.