Introduction

Topic Modeling is a process to automatically identify topics present in a text object and to derive hidden patterns exhibited by a text corpus. Topic Models are very useful for multiple purposes, including:

Document clustering
Organizing large blocks of textual data
Information retrieval from unstructured text
Feature selection

Imports

Import libraries and write settings here.

# Data manipulation
import pandas as pd
import numpy as np

# Options for pandas
pd.options.display.max_columns = None
pd.options.display.max_rows = None

pd.options.display.max_colwidth=-1

# Display all cell outputs
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = 'all'

from IPython import get_ipython
ipython = get_ipython()

# autoreload extension
if 'autoreload' not in ipython.extension_manager.loaded:
    %load_ext autoreload

%autoreload 2

# Visualizations
import plotly.express as px

import matplotlib.pyplot as plt
import seaborn as sns
import re


import gensim
from gensim import corpora

# libraries for visualization
import pyLDAvis
import pyLDAvis.gensim

Analysis

Load the zomato reviews data

We will load the cleaned data