class_docstr: Sentiment analyser inspired by the algorithm by Vivek Narayanan.
  
  This is the instantiated model of the :class:`.ViveknSentimentApproach`. For
  training your own model, please see the documentation of that class.
  
  The analyzer requires sentence boundaries to give a score in context.
  Tokenization is needed to make sure tokens are within bounds. Transitivity
  requirements are also required.
  
  For extended examples of usage, see the `Examples
  <https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/vivekn-sentiment/VivekNarayanSentimentApproach.ipynb>`__.
  
  ====================== ======================
  Input Annotation types Output Annotation type
  ====================== ======================
  ``TOKEN, DOCUMENT``    ``SENTIMENT``
  ====================== ======================
  
  Parameters
  ----------
  None
  
  References
  ----------
  The algorithm is based on the paper `"Fast and accurate sentiment
  classification using an enhanced Naive Bayes model"
  <https://arxiv.org/abs/1305.6143>`__.
  
  https://github.com/vivekn/sentiment/
code: def __init__(
      self,
      classname="com.johnsnowlabs.nlp.annotators.sda.vivekn.ViveknSentimentModel",
      java_model=None,
  ):
      super(ViveknSentimentModel, self).__init__(
          classname=classname, java_model=java_model
      )
  
file_path: spark-nlp/python/sparknlp/annotator/sentiment/vivekn_sentiment.py
imports: from sparknlp.common import *
  from sparknlp.common.annotator_type import AnnotatorType
label_desc: Use image node_id 1 to create a new ViveknSentimentModel object from inherited base classes: AnnotatorModel with example: obj = ViveknSentimentModel(classname, java_model)