How to do it...

Let's see how to generate text using Markov chains by performing the following steps:

1. Start by importing the markovify library and a text file whose style we would like to imitate:

import markovify
import pandas as pd

df = pd.read_csv("airport_reviews.csv")

As an illustration, I have chosen a collection of airport reviews as my text:

"The airport is certainly tiny! ..."

1. Next, join the individual reviews into one large text string and build a Markov chain model using the airport review text:

from itertools import chain

N = 100
review_subset = df["content"][0:N]
text = "".join(chain.from_iterable(review_subset))
markov_chain_model = markovify.Text(text)

Behind the scenes, the library computes the transition word probabilities from the text.

1. Generate five sentences using the Markov chain model:

for i in range(5):
    print(markov_chain_model.make_sentence())

1. Since we are using airport reviews, we will have the following as the output after executing the previous code:

On the positive side it's a clean airport transfer from A to C gates and outgoing gates is truly enormous - but why when we arrived at about 7.30 am for our connecting flight to Venice on TAROM.
The only really bother: you may have to wait in a polite manner.
Why not have bus after a short wait to check-in there were a lots of shops and less seating.
Very inefficient and hostile airport. This is one of the time easy to access at low price from city center by train.
The distance between the incoming gates and ending with dirty and always blocked by never ending roadworks.

Surprisingly realistic! Although the reviews would have to be filtered down to the best ones.

1. Generate 3 sentences with a length of no more than 140 characters:

for i in range(3):
    print(markov_chain_model.make_short_sentence(140))

With our running example, we will see the following output:

However airport staff member told us that we were put on a connecting code share flight.
Confusing in the check-in agent was friendly.
I am definitely not keen on coming to the lack of staff . Lack of staff . Lack of staff at boarding pass at check-in.