5 Best Ways to Extract Keywords from a List in Python

💡 Problem Formulation: Given a list of strings, each representing a block of text, the goal is to identify the most representative keywords within this collection. For instance, from the input ["Python programming basics", "Advanced Python data structures", "Understanding AI with Python"], the desired output could be a deduplicated list such as ["Python", "programming", "data", "structures", "AI"].

Method 1: Basic Looping and Filtering

This method involves iterating over each string in the list, breaking it down into individual words, and then filtering based on predefined criteria like word frequency or length. This is a simple and straightforward approach that doesn’t require any external libraries.

Here’s an example:

input_list = ["Python programming basics", "Advanced Python data structures", "Understanding AI with Python"]

keywords = set()
for phrase in input_list:
    words = phrase.split()
    for word in words:
        if len(word) > 3:
            keywords.add(word.lower())

print(keywords)

Output:

{"python", "programming", "basics", "advanced", "data", "structures", "understanding"}

This code snippet creates a set to store unique keywords. It iterates over the input list, splits each string into words, and adds words longer than three characters to the set in lowercase to avoid duplicates.

Method 2: Regular Expressions

Regular expressions can be used to extract words that match certain patterns. This method is powerful for complex pattern matching and can be fine-tuned to extract very specific keywords.

Here’s an example:

import re

input_list = ["Python programming basics", "Advanced Python data structures", "Understanding AI with Python"]
pattern = r'\b\w{4,}\b'  # Words with 4 or more characters

keywords = set()
for phrase in input_list:
    matches = re.findall(pattern, phrase)
    keywords.update([word.lower() for word in matches])

print(keywords)

Output:

{"python", "programming", "basics", "advanced", "data", "structures", "understanding"}

This snippet uses the regular expression pattern to match words with four or more characters. It finds all matches in each phrase and updates the set with the lowercased keywords, ensuring uniqueness.

Method 3: Using the Natural Language Toolkit (nltk)

The Natural Language Toolkit (NLTK) is a Python library designed for working with human language data. It offers tools to classify, tokenize, and identify keywords. This method provides a more sophisticated approach.

Here’s an example:

import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords

nltk.download('punkt')
nltk.download('stopwords')

input_list = ["Python programming basics", "Advanced Python data structures", "Understanding AI with Python"]
stop_words = set(stopwords.words('english'))

keywords = set()
for phrase in input_list:
    words = word_tokenize(phrase)
    keywords.update([word.lower() for word in words if word.lower() not in stop_words and word.isalpha()])

print(keywords)

Output:

{"python", "programming", "basics", "advanced", "data", "structures", "understanding"}

This code uses NLTK for tokenization and removes common English stopwords to extract more meaningful keywords. It checks if a word is alphabetic to exclude punctuation from the set.

Method 4: Using TextBlob for Keyword Extraction

TextBlob is a Python library for processing textual data. It provides a simple API for diving into common NLP tasks such as part-of-speech tagging, noun phrase extraction, and sentiment analysis. It can be particularly handy for extracting noun phrases which often represent key concepts.

Here’s an example:

from textblob import TextBlob

input_list = ["Python programming basics", "Advanced Python data structures", "Understanding AI with Python"]
keywords = set()

for phrase in input_list:
    blob = TextBlob(phrase)
    for np in blob.noun_phrases:
        keywords.add(np.lower())

print(keywords)

Output:

{"python programming", "advanced python", "data structures", "ai"}

In this snippet, TextBlob processes each phrase and extracts noun phrases, which are often keywords or key phrases. These phrases are added to a set to ensure uniqueness and are converted to lowercase to standardize the output.

Bonus One-Liner Method 5: List Comprehension with Sets

This one-liner approach utilizes Python’s set operations and list comprehension to provide a quick and concise way of extracting unique keywords from a list.

Here’s an example:

input_list = ["Python programming basics", "Advanced Python data structures", "Understanding AI with Python"]
keywords = {word.lower() for phrase in input_list for word in phrase.split() if len(word) > 3}

print(keywords)

Output:

{"python", "programming", "basics", "advanced", "data", "structures", "understanding"}

This compact snippet uses set comprehension to extract all words longer than three characters from each phrase in the input list, converting them to lowercase to remove duplicates.

Summary/Discussion

• Method 1: Basic Looping and Filtering. Pros: Simple and intuitive, no external dependencies. Cons: May not catch all relevant keywords and can be less efficient than other methods.
• Method 2: Regular Expressions. Pros: Highly customizable patterns, good for complex keyword extraction. Cons: Can be difficult to master for complex use cases, potentially slower performance.
• Method 3: Using the Natural Language Toolkit (nltk). Pros: Sophisticated tools for NLP, effective at extracting meaningful keywords. Cons: Requires additional installation of NLTK and downloading of necessary data sets.
• Method 4: Using TextBlob for Keyword Extraction. Pros: Easy to use, provides useful NLP functionalities. Cons: Less control over the extraction process compared to NLTK.
• Bonus Method 5: List Comprehension with Sets. Pros: Quick and concise. Cons: Limited customization and may not be suitable for complex scenarios.