The default model for sentiment analysis is a fine-tuned distilbert:

  
      github.com
  
  
    huggingface/transformers/blob/8065fea87007fbf7542fc060ff8ddd0b5df567da/src/transformers/pipelines.py#L2569-L2579 3

"sentiment-analysis": {
    "impl": TextClassificationPipeline,
    "tf": TFAutoModelForSequenceClassification if is_tf_available() else None,
    "pt": AutoModelForSequenceClassification if is_torch_available() else None,
    "default": {
        "model": {
            "pt": "distilbert-base-uncased-finetuned-sst-2-english",
            "tf": "distilbert-base-uncased-finetuned-sst-2-english",
        },
    },
},



  
  
    
    
  
  


It’s therefore no surprise that RoBERTa performs a lot better.
That being said, I can understand why the model thinks this is negative. “to remain silent” is often uttered in very negative context (“You have the right to remain silent” when the police arrests someone) Especially for smaller models, this can weigh heavily.