def METHOD_NAME(df, clutter_flag):
  nlp = spacy.load("en_core_web_sm")
  # Iterating through each row and doing preprocessing over it.
  # Picking out the manual tags from the csv and putting them into separate column "Original Tag"
  for index in df.index.to_list():
    text = df.loc[index, 'content_lower']
    doc = nlp(text)
    if type(text) == float:
      continue
    # Lemmatization
    lemma_list = []
    for token in doc:
      lemma_list.append(token.lemma_)
    # Filter the stopword
    filtered_list = []
    for word in lemma_list:
      lexeme = nlp.vocab[word]
      if lexeme.is_stop == False:
        filtered_list.append(word)
    # Remove punctuation
    punctuations = "?:!.,;'"
    for word in filtered_list:
      if word in punctuations:
        filtered_list.remove(word)
    # List joining and Filtering (c) and copyright unicode symbol
    copyright_string = " ".join(map(str, filtered_list))
    substring = "( c )"
    cp_symbol = '\xa9'  # Unicode for copyright Symbol
    # Replace ( c ) and the copyright unicode symbol © with "copyright"
    copyright_string = copyright_string.replace(substring, "copyright")
    copyright_string = copyright_string.replace(cp_symbol, "copyright")
    # Implementing NER and POS Tags after normalization
    doc2 = nlp(copyright_string)
    # All the NER taggings will be contained in a dictionary having "Entity" and "Values" as keys
    ent_dict = {}
    full_table_ner = {"Entity": [], "Values": []}
    for x in doc2.ents:
      ent_dict[x.text] = x.label_
    for key in ent_dict:
      full_table_ner["Entity"].append(key)
      full_table_ner["Values"].append(ent_dict[key])
    # All the POS taggings will be contained in a dictionary having "Entity" and "POS_TAGS" as keys
    pos_dict = {}
    full_table_pos = {"Entity": [], "POS_TAG": []}
    for token in doc:
      if not token.is_punct | token.is_space:
        pos_dict[token.text] = token.pos_
    for key in pos_dict:
      full_table_pos["Entity"].append(key)
      full_table_pos["POS_TAG"].append(pos_dict[key])
    # The checking function call happening with each iteration
    entityCheck(full_table_pos, full_table_ner, index, clutter_flag, df)
  print(df.to_json(orient='records'))