Text post-processing
Yes, you can do that by add them to the existing NLTK Stop-word dictionary for all such words/Creating a Custom Stop-word dictionary. 

for Custom Stop-word dictionary you need to include all the key words in the dictionary before processing, make sure to check if the words are present in the stop-word dictionary. If yes, remove them from the text else nothing needs to be done.


set of code is for updating new words to the NLTK Stop-words dictionary:

#Custom function to remove the stop-words
    def removeStopWords(str):
    #select english stopwords from NLTK
    cachedStopWords = set(stopwords.words("english"))
    #add custom words, mentioned above
    cachedStopWords.update(("viewed", "flutter", "function", "k", "neighbour"))
    #remove stop words
    new_str = ' '.join([word for word in str.split() if word not in cachedStopWords]) 
    return new_str
set of code is for making a Custom stop-word dictionary:

stopwordList = ["viewed", "flutter", "function", "k", "neighbour"]
StopWordsRemover(inputCol="words", outputCol="filtered" ,stopWords=stopwordList)