The mortgage industry deals with vast amounts of textual data – including investor reports, loan documents, operational logs, and customer feedback. Much of this information is unstructured, making it difficult to quickly identify trends, risks, and customer concerns. Manual analysis is time-consuming and often inconsistent.
Natural Language Processing (NLP) provides an effective solution by enabling automated extraction of meaningful keywords and phrases from large volumes of text. These keywords can reveal recurring issues, service gaps, and emerging business patterns.
In this blog, we demonstrate how to design a simple, yet practical keyword extraction pipeline tailored specifically for mortgage-related feedback using Python and NLTK.
In this Blog, We Will Show You How to:
- Preprocess customer comments and mortgage documents
- Normalize and clean unstructured text
- Extract and rank important keywords and phrases
- Interpret the results for business decision-making
Prerequisites:
- Before proceeding, ensure that you have:
- Basic understanding of Python programming and text processing concepts
- Python version 3.8 or higher installed
- Required libraries: nltk (Natural Language Toolkit), re (Regular Expressions), and collections (Counter).
Why Do We Need Keyword Extraction in Mortgage Solutions?
Mortgage platforms and servicing teams receive continuous streams of textual data from customers, auditors, and internal stakeholders. This data often contains valuable signals but remains hidden due to its unstructured nature.
By applying keyword extraction, organizations can:
- Identify recurring customer pain points such as late payments or approval delays
- Track frequently discussed topics including interest rates, documentation, and eligibility
- Support data-driven improvements in underwriting, servicing, and customer experience
NLP-based keyword extraction transforms raw text into structured insights that can be consumed by dashboards, analytics systems, and business teams.
NLP Keyword Extraction Components (Updated)
Dataset:
A dataset is a collection of feedback, comments, or reports that we want to analyze. In this example, it consists of mortgage-related customer comments covering approval processes, payment delays, service quality, and interest rate concerns. This dataset forms the raw input to our keyword extraction pipeline.
Target Function:
The target function defines the core logic of keyword extraction. It is responsible for:
- Cleaning and normalizing text
- Removing irrelevant words (stop words)
- Converting words into their base form (lemmatization)
- Counting word and phrase frequencies
The output is a ranked list of keywords and keyword pairs (bigrams) that represent dominant concepts in the dataset.
Evaluators (Detailed Explanation)
Evaluators are used to assess the quality and usefulness of the extracted keywords. Keyword extraction usually does not have labeled training data, so evaluation is heuristic and domain-driven rather than purely statistical.
In mortgage analytics, evaluators may include:
- Domain relevance check
Confirm that extracted keywords belong to mortgage and banking terminology such as interest rate, payment, foreclosure, credit score. This ensures that results are business-relevant rather than generic. - Frequency thresholding
Prioritize recurring keywords while deprioritizing words that appear only once. This highlights persistent customer concerns rather than isolated comments. - Manual expert validation
Mortgage analysts and operations teams review extracted keywords to confirm alignment with known operational issues such as documentation delays, portal downtime, or approval complexity.
Through these evaluators, the pipeline produces results that are not only statistically meaningful but also operationally actionable.
Environment Setup and Main Code
1. Install Dependencies
Install the required libraries using the Linux terminal:
pip install nltk
2. Set Up Your Environment
Before running the main code, download essential NLTK datasets:
import nltk
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('wordnet')
Explanation of Downloads
- punkt – Provides tokenizers that correctly split text into sentences and words
- stopwords – Supplies lists of common English words that typically carry little analytical value
- wordnet – Required for lemmatization to convert words into their dictionary base form.
3. Main Code – Target Function: Keyword Extraction
Below is the complete implementation of the keyword extraction pipeline used in this blog.
import nltk
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('wordnet')
from nltk.stem import WordNetLemmatizer
from nltk.util import ngrams
import re
from collections import Counter
# Sample mortgage-related customer comments
comments = [
"The loan approval process was smooth, but interest rates were high compared to other banks.",
"Mortgage portal was down for maintenance during payment due date, caused late payment charges.",
"Quick loan disbursal but customer service could improve on clarity of eligibility criteria.",
"Foreclosure policy is strict; early payment charges are very high.",
"Excellent mortgage service overall, but mobile app needs more features for EMI tracking.",
"Documentation process was lengthy, required too many supporting papers.",
"Interest rate offers for first-time buyers are competitive and attractive.",
"The pre-approval stage was confusing, needing more transparency on credit score requirements."
]
# Target function for keyword extraction
def extract_keywords(comments, top_n=20):
# Combine all comments into a single text
text = " ".join(comments)
# Remove URLs and special characters
text = re.sub(r'http\S+|www.\S+', '', text)
text = re.sub(r'[^a-zA-Z\s]', '', text.lower())
# Tokenize into words
words = text.split()
# Define simple stop words
stop_words = {
'the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at', 'to', 'for',
'of', 'with', 'by', 'was', 'are', 'is'
}
# Remove stop words and very short tokens
words = [word for word in words if word not in stop_words and len(word) > 2]
# Lemmatize words
lemmatizer = WordNetLemmatizer()
words = [lemmatizer.lemmatize(word) for word in words]
# Count word frequencies
word_freq = Counter(words)
top_keywords = word_freq.most_common(top_n)
# Extract bigrams (two-word phrases)
bigrams = Counter(ngrams(words, 2)).most_common(10)
return top_keywords, bigrams
# Run keyword extraction
keywords, top_bigrams = extract_keywords(comments)
print("Top Keywords:", keywords)
print("Top Bigrams:", top_bigrams)
Step-by-Step Explanation of the Code
Input Data
Mortgage-related customer comments are used as sample feedback for analysis.
Text Normalization
The comments are merged into one string, converted to lowercase, and stripped of punctuation and special characters to ensure consistency.
Tokenization and Stop Word Removal
The cleaned text is split into individual words. Common stop words and very short tokens are removed to retain only meaningful business terms.
Lemmatization
Lemmatization converts words into their base form while preserving meaning.
Examples:
- payments → payment
- charges → charge
- confusing → confuse
This prevents fragmentation of similar concepts across multiple word forms.
Output:
After running the script in the system, the following output is produced:
user@user-PC:-/Desktop/Test$/usr/local/bin/python3.10 /home/user/Desktop/Test/Main.
Top Keywords: [('payment', 3), ('loan', 2), ('process', 2), ('interest', 2),
('rate', 2), ('high', 2), ('mortgage', 2), ('charge', 2), ('service', 2),
('need', 2), ('more', 2), ('approval', 1), ('smooth', 1), ('were', 1),
('compared', 1), ('other', 1), ('bank', 1), ('portal', 1), ('down', 1),
('maintenance', 1)]
Top Bigrams: [(('interest', 'rate'), 2), (('payment', 'charge'), 2),
(('need', 'more'), 2), (('loan', 'approval'), 1), (('approval', 'process'), 1),
(('process', 'smooth'), 1), (('smooth', 'interest'), 1), (('rate', 'were'), 1),
(('were', 'high'), 1), (('high', 'compared'), 1)]
This output represents the most frequently occurring words and two-word phrases extracted from the mortgage-related customer comments after preprocessing and lemmatization.
Interpretation and Business Insights
This process shows how Python and NLTK can be used to clean text, normalize words, and extract important keywords and phrases from mortgage-related customer feedback.
However, the purpose of this output is not only to rank words, but to identify recurring customer concerns and operational issues.
Each repeated keyword or phrase represents a pattern in customer experience that can guide business decisions.
What Can We Derive from This Output?
1. Identifying Recurring Customer Pain Points
The word “payment” appears three times, making it the most frequent keyword in the dataset. This indicates that payment-related topics are a common concern among customers.
From this, a business user can infer:
- Customers may be confused about due dates or payment processes
- Late payment charges may be causing dissatisfaction
- Payment communication and workflows need closer monitoring
This insight helps teams prioritize improvements in payment processing and billing communication.
2. Understanding Pricing and Policy Sensitivity
Keywords such as interest, rate, high, and charge reflect strong customer sensitivity to pricing and fee structures.
From this, organizations can understand that:
- Interest rates are a major driver of customer sentiment
- High charges or penalties may impact satisfaction
- Pricing and fee communication may need improvement
This supports pricing strategy reviews and clearer customer communication.
3. Leveraging Bigrams for Precise Business Signals
The most frequent bigrams include:
- interest rate
- payment charge
- need more
- loan approval
- approval process
These phrases represent specific business problems and customer expectations.
They can be used to:
- Automatically categorize customer complaints
- Build dashboards showing major operational risk areas
Key Takeaways
- Install NLTK and download its datasets before running the script
- Text preprocessing is essential for accurate keyword extraction
- The script can be executed as a Python file in a Linux terminal
- Repeated keywords indicate systemic issues, not isolated complaints
- Bigrams provide actionable insights for operational improvement
Conclusion:
By applying this keyword extraction pipeline, organizations can transform unstructured mortgage feedback into structured and actionable insights. Instead of manually reading large volumes of comments, teams can quickly:
- Identify dominant customer pain points
- Detect operational bottlenecks early
- Support pricing, policy, and process optimization
This lightweight NLP approach provides a scalable foundation for continuous monitoring of customer experience in mortgage solutions.