Visualization of customer feedback to get insights of main reasons behind their feedback — Word Cloud
This article will focus on visualization of major reasons for customer giving negative reviews.
On technology front, I will be using “wordcloud” python library. I will be using airline industry dataset from kaggle.
When we think of airline industry, the common causes are flight delay, baggage issue, hospitality etc. For one airline provider, delay may be the major issue and for other it can be baggage issue. Here, we just try to represent the reasons in the form of word cloud.
As usual, data is pre-processed before applying analytics. I will skip data pre-processing details and focus on word cloud generation.
To create word cloud in python, we just need few lines of code.
from wordcloud import WordCloudwc = WordCloud(stopwords=stop_words, background_color="white", colormap="Dark2",max_font_size=150, random_state=42)wc.generate(data_nouns_adj.reviews[c])
Initially, I picked up nouns and adjectives expressed in customer reviews, and generated word cloud as shown below.
It did not give right sense as required for my use case. I wanted to understand the main pain points shared by customers.
Next trail, I picked the verbs and generated word cloud. This gave me better insights of customer reviews. As shown in below image, for few airlines, cancellation of flight is the main pain point, for few airlines, delay is the paint point.
(We can club delayed and waiting keywords as both words tend to mean same)
The word cloud is good visualization technique to understand text data. In this technique the size of each word indicates its frequency and significance.
Business Use Case for Word Cloud
1. Finding customer pain points — and opportunities to connect
2. Understanding how your employees feel about your company
3. Identifying new SEO terms to target
The more details can be found below