How to Build a Keyword Identification System in Python

To build a Keyword Identification System in Python, you can follow these general steps:

Keyword Identification System in Python

Step 1: Install Required Packages

You’ll need install NLTK (Natural Language Toolkit) package which is popular library for natural language processing.

Step 2: Load Text Data

You can load text data into your program using various methods such as reading from a file fetching from a website or database etc.

Step 3: Tokenization

Tokenization is process of splitting the text into individual words or tokens. You can use word_tokenize() function from NLTK package for this.

Step 4: Stopword Removal

Stopwords are common words that do not carry much meaning such as a, an, the, and, etc. You can remove stopwords using stopwords corpus from NLTK package.

Step 5: Stemming or Lemmatization

Stemming is process of reducing words to their root form (e.g., “running” to “run”), while lemmatization is process of reducing words to their base form (e.g., “ran” to “run”). You can use PorterStemmer or WordNetLemmatizer classes from the NLTK package.

Step 6: Count Frequency of each Keyword

You can use Python’s built-in collections module or FreqDist() function from the NLTK package to count the frequency of each keyword.

Here some sample code to give you an idea of how to implement a keyword identification system in Python using NLTK package:

import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from collections import Counter

# Step 1: Load the text data
text = "The quick brown fox jumps over the lazy dog. The dog, however, is not impressed."

# Step 2: Tokenization
tokens = word_tokenize(text)

# Step 3: Stopword removal
stop_words = set(stopwords.words('english'))
filtered_tokens = [token for token in tokens if token.lower() not in stop_words]

# Step 4: Stemming
stemmer = PorterStemmer()
stemmed_tokens = [stemmer.stem(token) for token in filtered_tokens]

# Step 5: Count the frequency of each keyword
frequency = Counter(stemmed_tokens)
print(frequency)

This program will output frequency of each keyword in text:

Counter({'dog': 2, '.': 2, ',': 2, 'quick': 1, 'brown': 1, 'fox': 1, 'jump': 1, 'lazi': 1, 'howev': 1, 'impress': 1})

You can run this code in your local Python environment or using online tools like Google Colab.

I hope this helps you build your own Keyword Identification System in Python!

Here are some useful links related to building a Keyword Identification System in Python using NLTK:

NLTK documentation: https://www.nltk.org/
NLTK book: https://www.nltk.org/book/
Tokenization in NLTK: https://www.nltk.org/api/nltk.tokenize.html
Stopwords in NLTK: https://www.nltk.org/book/ch02.html#stopwords_index_term
Stemming in NLTK: https://www.nltk.org/howto/stem.html
Counter module in Python: https://docs.python.org/3/library/collections.html#collections.Counter
Google Colab: https://colab.research.google.com/

I hope you find these links helpful in building your own Keyword Identification System in Python!