utils4text.py
def text_lemmatize(text, lemmatizer):
    """
    Apply lemmatization on the raw texts to convert the words in texts back to their root
    forms. Before lemmatization, remove unnecessary marks and stopwords to keep only the 
    meaningful words.

    Arg:
        text: A string text.
        lemmatizer: An object of WordNetLemmatizer.
    Return:
        lem_words: A list of lemmatized words.
    """
    # remove unnecessary marks and tokenize
    tokens = word_tokenize(remove_marks(text))

    # remove stopwords
    filtered_tokens = [word for word in tokens if word not in stopwords.words('english')]

    # lemmatize the tokenized texts
    lem_words = []
    lem_words += list(map(token_lemmatize, filtered_tokens, repeat(lemmatizer)))
        
    return lem_words

    Apply lemmatization on the raw texts to convert the words in texts back to their root
    forms. Before lemmatization, remove unnecessary marks and stopwords to keep only the 
    meaningful words.

    Arg:
        text: A string text.
        lemmatizer: An object of WordNetLemmatizer.
    Return:
        lem_words: A list of lemmatized words.
    
Apply lemmatization on the raw texts to convert the words in texts back to their root
forms. Before lemmatization, remove unnecessary marks and stopwords to keep only the
meaningful words.

A string text.
lemmatizer: An object of WordNetLemmatizer.
Return:
lem_words: A list of lemmatized words.
Apply lemmatization on the raw texts to convert the words in texts back to their root forms . Before lemmatization remove unnecessary marks and stopwords to keep only the meaningful words . A string text . lemmatizer : An object of WordNetLemmatizer . Return : lem_words : A list of lemmatized words .
def text_lemmatize(text, lemmatizer):
    tokens = word_tokenize(remove_marks(text))
    filtered_tokens = [word for word in tokens if word not in stopwords.words('english')]
    lem_words = []
    lem_words += list(map(token_lemmatize, filtered_tokens, repeat(lemmatizer)))
    return lem_words
def text_lemmatize ( text , lemmatizer ) : tokens = word_tokenize ( remove_marks ( text ) ) filtered_tokens = [ word for word in tokens if word not in stopwords . words ( 'english' ) ] lem_words = [ ] lem_words += list ( map ( token_lemmatize , filtered_tokens , repeat ( lemmatizer ) ) ) return lem_words
Apply lemmatization on the raw texts to convert the words in texts back to their root
forms.
Apply lemmatization on the raw texts to convert the words in texts back to their root forms .
"""
    Apply lemmatization on the raw texts to convert the words in texts back to their root
    forms. Before lemmatization, remove unnecessary marks and stopwords to keep only the 
    meaningful words.

    Arg:
        text: A string text.
        lemmatizer: An object of WordNetLemmatizer.
    Return:
        lem_words: A list of lemmatized words.
    """ # remove unnecessary marks and tokenize # remove stopwords # lemmatize the tokenized texts
text
lemmatizer