Building a text generator using LSTMs

Text generation is a unique problem wherein, given some data, we should be able to predict the next occurring data. Good examples of where text generation is required include predicting the next word in our mobile phone keyboards, generating stories, music, and lyrics and so on. Let's try to build a model that can generate text related to describing hotels for the city of Mumbai, as follows:

1. We will begin by importing the various libraries we will be using during the course of solving this problem, as follows:

import nltk
from nltk.corpus import stopwords
import pandas as pd
import numpy as np
import re
from keras.preprocessing.sequence import pad_sequences
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense, LSTM, Dropout, Embedding

2. Now that we have loaded our libraries, let's load our dataset. For this exercise, we will use the Hotels on MakeMyTrip dataset, obtained from https://data.world/promptcloud...