In this article, I will take you through a Machine Learning project on Keyword Extraction with Python programming language. In machine learning, Keyword extraction is a task of Natural Language Processing.
What is Keyword Extraction?
Keyword extraction is defined as the task of Natural language processing that automatically identifies a set of terms to describe the subject of the text. This is an important method in information retrieval (IR) systems: keywords simplify and speed up research. Keyword extraction can be used to reduce text dimensionality for further text analysis (subject modeling text classification).
Also, Read – 100+ Machine Learning Projects Solved and Explained.
The task of keyword extraction can be used in automatically indexing data, summarizing text, or generating tag clouds with the most representative keywords.
Machine Learning Project on Keyword Extraction with Python
Now, in this section, I will take you through a Machine Learning project on Keyword Extraction with Python programming language. I will start by importing the necessary libraries and the dataset:
import numpy as np # linear algebra
import pandas as pd # data processing
df = pd.read_csv('papers.csv')This dataset contains 7 columns: id, year, title, even_type, pdf_name, abstract and paper_text. We are mainly interested in the paper_text which includes both the title and the abstract.
The next step is to preprocess our textual data. For this task, I will use the NLTK library in Python:
Using TF-IDF
TF-IDF stands for Text Frequency Inverse Document Frequency. The importance of each word increases in proportion to the number of times a word appears in the document (Text Frequency – TF) but is offset by the frequency of the word in the corpus (Inverse Document Frequency – IDF).
Using the tf-idf weighting scheme, the keywords are the words with the highest TF-IDF score. For this task, I’ll first use the CountVectorizer method in Scikit-learn to create a vocabulary and generate the word count:
Now I’m going to use the TfidfTransformer in Scikit-learn to calculate the reverse frequency of documents:
Now, we are ready for the final step. In this step, I will create a function for the task of Keyword Extraction with Python by using the Tf-IDF vectorization:
===Keywords=== update rule 0.344 update 0.285 auxiliary 0.212 non negative matrix 0.21 negative matrix 0.209 rule 0.192 nmf 0.183 multiplicative 0.175 matrix factorization 0.163 matrix 0.163
I hope you liked this article on the Machine Learning project on Keyword Extraction with Python programming language. Feel free to ask your valuable questions in the comments section below.
