class_docstr: interface to Sentiment Analyzer
docstr: ```
  ImageCaptioner constructor
  
  Args:
    device(str): device to use (e.g., 'cuda', 'cpu')
  ```
code: def __init__(self, device=None, **kwargs):
      """
      ```
      ImageCaptioner constructor
  
      Args:
        device(str): device to use (e.g., 'cuda', 'cpu')
      ```
      """
  
      super().__init__(
          device=device,
          quantize=False,
          min_transformers_version="4.12.3",
      )
      self.pipeline = pipeline(
          "text-classification",
          model="cardiffnlp/twitter-roberta-base-sentiment",
          device=self.device_to_id(),
          **kwargs,
      )
      self.mapping = {
          "LABEL_0": "NEGATIVE",
          "LABEL_1": "NEUTRAL",
          "LABEL_2": "POSITIVE",
      }
  
file_path: ktrain/ktrain/text/sentiment/core.py
imports: from typing import Union
  from transformers import pipeline
  from ...None import utils
  from ...torch_base import TorchBase
label_desc: Use image node_id 1 to create a new SentimentAnalyzer object from inherited base classes: TorchBase with example: obj = SentimentAnalyzer(device)