Topic modeling is a type of statistical modeling for discovering abstract “subjects” that appear in a collection of documents. This means creating one topic per document template and words per topic template, modeled as Dirichlet distributions. In this article, I will walk you through the task of Topic Modeling in Machine Learning with Python.
The field of Topic modeling has become increasingly important in recent years. Subject modeling is an unsupervised machine learning way to organize text (or image or DNA, etc.) information so that associated pieces of text can be identified.
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What is Topic Modeling?
In machine learning and natural language processing, topic modeling is a type of statistical model for discovering abstract subjects that appear in a collection of documents. Topic modeling is a text mining tool frequently used for discovering hidden semantic structures in body text.
Intuitively, since a document is about a particular topic, one would expect that particular words would appear more or less frequently in the document: “dog” and “bone” will appear more often in documents about dogs, “Cat” and “meow” will appear in chat documents, and “the” and “is” will appear roughly equally in both.
A document generally concerns several subjects in different proportions; thus, in a 10% cat and 90% dog document, there would probably be about 9 times more dog words than cat words. The “topics” produced by topic modeling techniques are groups of similar words.
A topic modeling machine learning model captures this intuition in a mathematical framework, which makes it possible to examine a set of documents and discover, based on the statistics of each person’s words, what the subjects might be and what the balance of the subjects of the subject is. each document.
Topic Modeling with Python
Now, I will take you through a task of topic modeling with Python programming language by using a real-life example. I will be performing some modeling on research articles. The dataset I will use here is taken from kaggle.com. You can easily download all the files that I am using in this task from here.
Now let’s get started with the task of Topic Modeling with Python by importing all the necessary libraries that we need for this task:
Now, the next step is to read all the datasets that I am using in this task:
Exploratory Data Analysis
Exploratory Data Analysis explores the data to find the relationship between measures that tell us they exist, without the cause. They can be used to formulate hypotheses. EDA helps you discover relationships between measures in your data, which do not prove the existence of correlation, as indicated by the expression.
Now I will perform some EDA to find some patterns and relationships in the data before getting into topic modeling:
print(train.isna().sum)Code language: Python (python)<bound method DataFrame.sum of id ABSTRACT … Superconductivity Systems and Control 0 False False … False False 1 False False … False False 2 False False … False False 3 False False … False False 4 False False … False False … … … … … … 13999 False False … False False 14000 False False … False False 14001 False False … False False 14002 False False … False False 14003 False False … False False [14004 rows x 31 columns]>
print(test.isna().sum)Code language: Python (python)<bound method DataFrame.sum of id ABSTRACT Computer Science Mathematics Physics Statistics 0 False False False False False False 1 False False False False False False 2 False False False False False False 3 False False False False False False 4 False False False False False False … … … … … … … 5997 False False False False False False 5998 False False False False False False 5999 False False False False False False 6000 False False False False False False 6001 False False False False False False [6002 rows x 6 columns]>
There is great variability in the number of characters in the Abstracts of the Train set. We have a minimum of 54 to a maximum of 4551 characters on the train. The median number of characters is 1065.
The test set looks better than the training set as the minimum number of characters in the test set is 46, while the maximum is 2841. So the median number of characters in the test set is 1058, which is very similar to the training set.
The learning set has a similar trend in the number of words as we have seen in the number of characters. Minimum of 8 words and maximum of 665 words. So the median word count is 153.
Minimum of 7 words in an abstract and maximum of 452 words in the test set. The median here is exactly the same as that observed in the training set and is equal to 153.
Topic Modeling Using Tags
There are a lot of methods of topic modeling. I will use the tags in this task, let’s see how to do this by exploring the tags:
So this is how we can perform the task of topic modeling by using the Python programming language. I hope you liked this article on Topic Modeling in machine learning with Python. Feel free to ask your valuable questions in the comments section below.
