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# Customer Satisfaction Measurement with N-gram and Sentiment Analysis
Product reviews are an excellent source of information for qualified management decisions. Learn more about the right text mining...
[Petr Koráb](https://towardsdatascience.com/author/petrkorab/)
April 10, 20237 min read
### Product reviews are an excellent source of information for qualified management decisions. Learn more about the right text mining techniques.
Photo by [Freepik](https://www.freepik.com/free-photo/collage-customer-experience-concept_25053683.htm#query=customer%20satisfaction%20data&position=5&from_view=search&track=ais) on Freepik
Learn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) roadmap.
## Introduction
Happy customers drive company growth. The five-word sentence explains everything about why we do our best to maximize customer satisfaction. Product reviews are one of the major data sources that large companies like [Amazon](https://www.amazon.com/gp/help/customer/display.html?nodeId=GL4WJF8BGV8VL6B8) and [Apple](https://www.apple.com/contact/feedback/#:~:text=To%20comment%20on%20a%20particular,and%20select%20the%20appropriate%20link.), middle-sized exporters including [Lentiamo](https://www.trustpilot.com/review/lentiamo.nl), and local companies running their Facebook pages collect. Reviews are typically collected repeatedly over time, and factors like quality shifts, marketing communications, and customer care friendliness impact the sentiment expressed by customers.
Note: Image by author, based on the review of Karim (2011), Baker and Wurgler (2006), Merrin et al. (2013), and Eachempat et al. (2022)
The Business Intelligence (BI) role should be to analyze product reviews, identify potential problems, and develop hypotheses to solve them. In the next stage, these recommendations are scrutinized by other departments based on the company structure. _**This article will explain more closely the analytics of customer satisfaction measurement with product reviews data.**_
The-end-to end process includes:
* How to make an exploratory analysis of time-series product reviews
* How to quickly evaluate sentiment in product reviews over time
* How to display the most frequent satisfaction factors over time
We'll work in Python, which most BI and data analysts routinely use.
## 2. Data
Getting unscraped product reviews data with a flexible data license is generally difficult. Synthetic data from [**Fake Reviews Dataset**](https://osf.io/tyue9/) distributed with [Attribution 4.0 International license](https://creativecommons.org/licenses/by/4.0/) is an excellent option in this case.
The data looks like this:
Image 1. Fake Reviews Dataset
The _text_ column contains product reviews, and _period_ marks the review date. The subset we'll work with contains 3848 reviews of clothing, shoes, and jewelry products.
## 3. Exploratory data analysis (EDA)
EDA in this type of dataset should discover the **completeness** of the dataset for each period of interest and the **presence of noise** that does not bring valuable information – digits and special characters. We try to avoid the situation of highly imbalanced datasets containing very few reviews in some periods and noisy datasets with too many digits and special characters (/, @, &,;, ? etc.).
**3.1. Checking for data completeness**
First, let's check the data for completeness for each period. We make annual comparisons and therefore summarise the reviews for each year:
python
```
import pandas as pd
# Calculate review frequencies by year
data['year'] = pd.DatetimeIndex(data['period']).year
rows_count = data.groupby('year', as_index=False).year.value_counts()
rows_count.columns=['year','reviews']
import matplotlib.pyplot as plt
# Generate a line plot
rows_count.plot.line(x='year',y = 'reviews')
plt.show()
```
A simple line pot shows the yearly review frequencies. It is not necessary to make any graph formatting here:
Image 2. Yearly product review frequencies. Image by author.
* * *
> The dataset is not largely imbalanced. We have about 200 and more reviews per year, which makes a solid dataset for sentient and n-gram analyses.
**3.2. Calculating letter-to-other characters ratio**
Next, let's check if the data does not consist mainly of numbers and special characters, which could bias later text mining procedures.
python
```
import re
# Convert product reviews to a string
text = pd.Series.to_string(data['text'], index = False)
# Remove newline characters
text = re.sub('n', '', text)
# Calculate sum of numbers, letters, and spaces
numbers = sum(c.isdigit() for c in text)
letters = sum(c.isalpha() for c in text)
spaces = sum(c.isspace() for c in text)
others = len(text) - numbers - letters - spaces
```
We calculate _cleanness_ and _dirtiness_ (the opposite) metrics that show the ratios of letters to other characters (numbers, spaces, and special characters):
python
```
# Calculate metrics
dirtiness = ((numbers + others) / len(text)) * 100
cleanness = ((letters + spaces) / len(text)) * 100
print(dirtiness)
print(cleanness)
```
The print output is:
* dirtiness is 8.77 %
* cleanness is 91.22 %
> We have around 9 % of digits and special characters in the data. This volume of noise should not bias sentiment analysis in the later stage.
## 4. Customer satisfaction measurement
Text mining attempts to find out: (1) how the customers' sentiment developed over time and (2) which factors contributed to changes in customer satisfaction. We'll use [Arabica](https://pypi.org/project/arabica/), a Python library for time-series text mining, to explore both.
_**EDIT Jul 2023**_ _: Arabica has been updated. Check the_ _**documentation**_ _for the full list of parameters._
### 4.1. Sentiment analysis
Arabica offers `coffee_break` module for sentiment and breakpoint analysis. Its [documentation](https://arabica.readthedocs.io/en/latest/Breakpoint%20identification.html) provides more details about the models and methodology of sentiment evaluation.
This code cleans data from punctuation and numbers, removes other redundant strings (_"_
_", "/n",_ and _"Another Long String"_ are used as examples), calculates sentiment for each review, aggregates the sentiment by year, and identifies two major break points in the sentiment time series:
python
```
from arabica import coffee_break
coffee_break(text = data['text'],
time = data['period'],
date_format = 'us', # Use US-style date format to read dates
preprocess = True, # Clean data - digits and punctuation
skip = ['
', # Remove additional stop words
'/n',
'Another Long String'],
n_breaks = 2, # 2 breakpoints identified
time_freq = 'Y') # Yearly aggregation
```
The line plot with sentiment and breakpoints:
Image 3. Sentiment analysis with breakpoints. Image by author.
We can see fluctuations in sentiment over time. These changes are not rapid as sentiment varies [0.6: 0.7]. The higher value of sentiment, the better customers perceive the products (and the opposite).
> In 1991 and 2000, there were positive and negative breakpoints in sentiment. In the next stage, let's check what caused these shifts.
### 4.2. Factors driving customer satisfaction
We'll use a heatmap of n-gram-frequencies from the `cappuccino` module to visually derive the factors that influenced customers' sentiment. Read the [documentation](https://arabica.readthedocs.io/en/latest/Heatmap.html) for more technical details.
This code plots a heatmap of the eight most frequent bigrams (i.e., two consecutive words) in each year. The data pre-processing includes cleaning from English stopwords, numbers, and three unwanted strings (_"_
_", "/n"_ , and _"Another Long String"_). Punctuation and blank rows are removed automatically:
python
```
from arabica import cappuccino
cappuccino(text = data['text'],
time = data['period'],
date_format = 'us', # Use US-style date format to parse dates
plot = 'heatmap',
ngram = 2, # N-gram size, 1 = unigram, 2 = bigram
time_freq = 'Y', # Aggregation period, 'M' = monthly, 'Y' = yearly
max_words = 8, # Display 8 most frequent bigrams for each period
stopwords = ['english'], # Remove English stopwords
skip = ['
', # Remove additional stop words
'/n',
'Another Long String'],
numbers = True, # Remove numbers
lower_case = True) # Lowercase text
```
The bigram heatmap:
Image 4. Bigram heatmap. Image by author.
Bigram frequencies indicate that:
* _**2000s drop**_ (missing _"would recommend", "fits perfectly", and "fit perfect"_ in the top 8), and high frequencies of _"wide foot"_ and _"size fit"_ suggest that we might be selling products to people that do not fit their size of foot. Note that we removed stop words such as _"don't"_.
## Conclusions
This type of text-mining analytics should help make qualified **data-informed decisions** and improve the quality of our products and services. Based on the analytical results, we determine a set of hypotheses about what might be wrong and where is space for improvement.
From the text mining analysis in this article, we can see potential problems with product quality. The products fit worse our customers' current needs. Receiving the reviews in 2001, the analysts should formulate a set of problem hypotheses such as:
* _The new shoes don't fit well._
* _We don't sell wide-foot shoes, but customers demand them._
* _There's a quality problem in the production of new products._
Many other potential problem hypotheses arise since you know your products better than anyone else. Other departments (Marketing, Customer Care, Logistics, ..) should find out where the problem is, then react with specific improvements, and fix the problem. The response, of course, depends on the company's size and structure and other firm-specific factors.
The jupyter notebook with the code and data is on my [GitHub](https://github.com/PetrKorab/Customer-Satisfaction-Measurement-with-N-gram-and-Sentiment-Analysis).
_Did you like the article? You can invite me_[ _for coffee_](https://www.buymeacoffee.com/petrkorab) _and support my writing. You can also subscribe to my_[ _email list_](https://medium.com/subscribe/@petrkorab) _to get notified about my new articles. Thanks!_
## References
Baker, M., Wurgler, J., 2006. Investor sentiment and the cross-section of stock returns. _Journal of Finance_ 61 (4).
Eachempati, P., Srivastava, P. R., Kumar A., Munoz, J., Dursun D., 2022. [Can customer sentiment impact firm value?](https://www.sciencedirect.com/science/article/abs/pii/S0040162521006995?via%3Dihub) An integrated text mining approach. _Technological Forecasting & Social Change_ 174 (1).
Karim, B., 2011. Corporate name change and shareholder wealth effect: empirical evidence in the French stock market. _Journal of Asset Management_ 12 (3).
Merrin, R. P., Hoffmann, A. O., Pennings, J. M., 2013. Customer satisfaction as a buffer against sentimental stock-price corrections. _Marketing Letters_ 24 (1).
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* * *
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## [Petr Koráb](https://towardsdatascience.com/author/petrkorab/)
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[Hannah Yan Han](https://towardsdatascience.com/author/yanhann10/)
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* [](https://towardsdatascience.com/plotting-maps-with-geopandas-428c97295a73/)
## [Plotting Maps with GeoPandas](https://towardsdatascience.com/plotting-maps-with-geopandas-428c97295a73/)
[](https://towardsdatascience.com/customer-satisfaction-measurement-with-n-gram-and-sentiment-analysis-547e291c13a6/)
Beginners Guide to Geospatial Data Plotting
[Ryan Lewis](https://towardsdatascience.com/author/rylewww/)
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* [](https://towardsdatascience.com/five-essential-things-you-must-know-to-learn-tableau-2b899403bfcb/)
## [Five essential things you must know to learn Tableau](https://towardsdatascience.com/five-essential-things-you-must-know-to-learn-tableau-2b899403bfcb/)
[Data Science](https://towardsdatascience.com/category/data-science/)
A guide with free resources for building your skills
[Brian Perron, PhD](https://towardsdatascience.com/author/b-r-i-a-n/)
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* [](https://towardsdatascience.com/3-tips-you-need-to-be-successful-in-data-visualization-9b5225ab7afc/)
## [3 Tips You Need to Be Successful in Data Visualization](https://towardsdatascience.com/3-tips-you-need-to-be-successful-in-data-visualization-9b5225ab7afc/)
[Programming](https://towardsdatascience.com/category/programming/)
Your data is good - but you can make it better.
[Benjamin Nweke](https://towardsdatascience.com/author/benjaminnweke/)
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* [](https://towardsdatascience.com/how-to-get-into-data-science-without-a-degree-2f84516f1afd/)
## [How to Get Into Data Science Without a Degree](https://towardsdatascience.com/how-to-get-into-data-science-without-a-degree-2f84516f1afd/)
[Programming](https://towardsdatascience.com/category/programming/)
Advice from a Data Scientist in the same position
[Terence Shin, MSc, MBA](https://towardsdatascience.com/author/terenceshin/)
October 29, 20207 min read
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