ModuleNotFoundError: No module named 'textblob'

>>> from lexicalrichness import LexicalRichness

# text example
>>> text = """Measure of textual lexical diversity, computed as the mean length of sequential words in
                a text that maintains a minimum threshold TTR score.

                Iterates over words until TTR scores falls below a threshold, then increase factor
                counter by 1 and start over. McCarthy and Jarvis (2010, pg. 385) recommends a factor
                threshold in the range of [0.660, 0.750].
                (McCarthy 2005, McCarthy and Jarvis 2010)"""

# instantiate new text object (use the tokenizer=blobber argument to use the textblob tokenizer)
>>> lex = LexicalRichness(text)

# Return word count.
>>> lex.words
57

# Return (unique) word count.
>>> lex.terms
39

# Return type-token ratio (TTR) of text.
>>> lex.ttr
0.6842105263157895

# Return root type-token ratio (RTTR) of text.
>>> lex.rttr
5.165676192553671

# Return corrected type-token ratio (CTTR) of text.
>>> lex.cttr
3.6526846651686067

# Return mean segmental type-token ratio (MSTTR).
>>> lex.msttr(segment_window=25)
0.88

# Return moving average type-token ratio (MATTR).
>>> lex.mattr(window_size=25)
0.8351515151515151

# Return Measure of Textual Lexical Diversity (MTLD).
>>> lex.mtld(threshold=0.72)
46.79226361031519

# Return hypergeometric distribution diversity (HD-D) measure.
>>> lex.hdd(draws=42)
0.7468703323966486

# Return Herdan's lexical diversity measure.
>>> lex.Herdan
0.9061378160786574

# Return Summer's lexical diversity measure.
>>> lex.Summer
0.9294460323356605

# Return Dugast's lexical diversity measure.
>>> lex.Dugast
43.074336212149774

# Return Maas's lexical diversity measure.
>>> lex.Maas
0.023215679867353005

# Your preferred preprocessing + tokenization pipeline
def nlp_pipeline(text):
        ...
        return list_of_tokens

# Initiate new LexicalRichness object with your preprocessing pipeline as input
lex = LexicalRichness(text, preprocesser=None, tokenizer=nlp_pipeline)

# Compute lexical richness
mtld = lex.mtld()

# Preprocess the text
list_of_tokens = nlp_pipeline(text)

# Initiate new LexicalRichness object with your list of tokens as input
lex = LexicalRichness(list_of_tokens, preprocesser=None, tokenizer=None)

# Compute lexical richness
mtld = lex.mtld()

>>> import inspect

# docstring for hdd (HD-D)
>>> print(inspect.getdoc(LexicalRichness.hdd))

Hypergeometric distribution diversity (HD-D) score.

For each term (t) in the text, compute the probabiltiy (p) of getting at least one appearance
of t with a random draw of size n < N (text size). The contribution of t to the final HD-D
score is p * (1/n). The final HD-D score thus sums over p * (1/n) with p computed for
each term t. Described in McCarthy and Javis 2007, p.g. 465-466.
(McCarthy and Jarvis 2007)

Parameters
__________
draws: int
    Number of random draws in the hypergeometric distribution (default=42).

Returns
_______
float

>>> print(lex.hdd.__doc__)

Hypergeometric distribution diversity (HD-D) score.

    For each term (t) in the text, compute the probabiltiy (p) of getting at least one appearance
    of t with a random draw of size n < N (text size). The contribution of t to the final HD-D
    score is p * (1/n). The final HD-D score thus sums over p * (1/n) with p computed for
    each term t. Described in McCarthy and Javis 2007, p.g. 465-466.
    (McCarthy and Jarvis 2007)

    Parameters
    ----------
    draws: int
        Number of random draws in the hypergeometric distribution (default=42).

    Returns
    -------
    float