What is Topic Modelling in NLP?

When we conduct a real-life discussion with someone, we talk about any topic to elicit some inner meaning. Similarly, a subject in NLP denotes a group of words that are connected in some manner.

 

A topic model finds subjects in a set of documents automatically. Following that, a trained model may be used to determine which of these subjects appear in fresh papers. The model may also determine whether parts of a document are relevant to particular themes. In this blog, we are going to learn about topic modeling.

 

What is Topic Modelling?

 

Topic modeling is an unsupervised machine learning approach that can scan a series of documents, find word and phrase patterns within them, and automatically cluster word groupings and related expressions that best represent the set. 

 

Because it doesn't require a preexisting list of tags or training data that has been previously categorized by humans, this type of machine learning is known as 'unsupervised' machine learning.

 

But this is not to be confused with the different topic classification models, which are ‘supervised’ machine learning techniques. Before studying a series of writings, it's necessary to know what they're about. Data is manually labeled with these themes so that a topic classifier can learn and make predictions on its own.

 

Topic modeling is the method of extracting needed attributes from a bag of words. This is critical because each word in the corpus is treated as a feature in NLP. As a result, feature reduction allows us to focus on the relevant material rather than wasting time sifting through all of the data's text.

 

(Related read: Top 10 Data Extraction Tools)

 

Working and Methods of Topic Modeling:

 

To infer subjects from unstructured data, topic modeling includes counting words and grouping similar word patterns. Suppose, if we are a software firm interested in learning what consumers have to say about specific elements of our product, we would need to use a topic modeling algorithm to examine our comments instead of spending hours trying to figure out which messages are talking about our topics of interest. 

 

A topic model groups feedback that is comparable, as well as phrases and expressions that appear most frequently, by recognizing patterns such as word frequency and distance between words. We may rapidly infer what each group of texts is about using this information.
 

Five algorithms are particularly used for topic modeling. We are going to learn about the methods, taking help from OpenGenus.

 

1. Latent Dirirchlet Allocation (LDA):

 

The statistical and graphical concept of Latent Dirichlet Allocation is used to find correlations between many documents in a corpus. The greatest likelihood estimate from the entire corpus of text is obtained using the Variational Exception Maximization (VEM) technique. 

 

This is traditionally solved by selecting the top few words from a bag of words. The statement, however, is utterly devoid of meaning. Each document may be represented by a probabilistic distribution of subjects, and each topic can be defined by a probabilistic distribution of words, according to this approach. As a result, we have a much better picture of how the issues are related.

---

Example of LDA (Source)

---

Consider the following scenario: you have a corpus of 1000 documents. The bag of words is made up of 1000 common words after preprocessing the corpus. We can determine the subjects that are relevant to each document using LDA. 

 

The extraction of data from a corpus of data is therefore made straightforward. The upper level represents the documents, the middle level represents the produced themes, and the bottom level represents the words in the diagram above. 

 

As a result, the rule indicates that a text is represented as a distribution of themes, and topics are described as a distribution of words.

 

2. Non Negative Matrix Factorization (NMF):

 

NMF is a matrix factorization method that ensures the non-negative elements of the factorized matrices. Consider the document-term matrix produced after deleting stopwords from a corpus. The term-topic matrix and the topic-document matrix are two matrices that may be factored out of the matrix. 

 

Matrix factorization may be accomplished using a variety of optimization methods. NMF may be performed more quickly and effectively using Hierarchical Alternating Least Square. The factorization takes place in this case by updating one column at a time while leaving the other columns unchanged.

 

3. Latent Semantic Analysis (LSA):

 

Latent Semantic Analysis is another unsupervised learning approach for extracting relationships between words in a large number of documents. This assists us in selecting the appropriate documents. 

 

It merely serves as a dimensionality reduction tool for the massive corpus of text data. These extraneous data adds noise to the process of extracting the proper insights from the data.

 

4. Parallel Latent Dirichlet Allocation:

 

Partially Labeled Dirichlet Allocation is another name for it. The model implies that there are a total of n labels, each of which is associated with a different subject in the corpus. 

 

Then, similar to the LDA, the individual themes are represented as the probability distribution of the entire corpus. Optionally, each document might be allocated a global subject, resulting in l global topics, where l is the number of individual documents in the corpus.

 

The technique also assumes that every subject in the corpus has just one label. In comparison to the other approaches, this procedure is highly rapid and exact because the labels are supplied before creating the model.

 

5. Pachinko Allocation Model (PAM):

 

The Pachinko Allocation Model (PAM) is a more advanced version of the Latent Dirichlet Allocation Model. The LDA model identifies themes based on thematic correlations between words in the corpus, bringing out the correlation between words. PAM, on the other hand, makes do by modeling correlation between the produced themes. Because it additionally considers the link between subjects, this model has more ability in determining the semantic relationship precisely.

 

Pachinko is a popular Japanese game, and the model is named for it. To explore the association between themes, the model uses Directed Acrylic Graphs (DAG). 

 

(Suggested reading: NLP techniques for information extraction)

 

Topic modeling vs Topic classification:

 

As we have talked about this supervised learning technique called topic classification before, it is not to be confused with topic modeling. 

 

Unsupervised machine learning techniques like topic modeling, on the other hand, require less user input in principle than supervised algorithms. This is because they don't require human training with manually tagged data. They do, however, require high-quality data, and they require it in large quantities, which may not always be simple to get by.

 

On the other side, supervised machine learning algorithms produce beautifully packaged findings with topic labels like Price and UX. Yes, they take longer to set up since they must be trained by tagging datasets with a preset range of subjects. 

 

However, if you carefully label your texts and tweak your criteria, you'll be rewarded with a model that can successfully categorize unknown texts based on their subjects.

 

The only similarity between both of these analysis techniques is that both of them are very commonly used by organizations in the market. 

 

Topic modeling APIs:

 

APIs (application programming interfaces) are a terrific method to link apps and enhance the functionality of your apps. There is a slew of topic modeling tools with their APIs, as well as a variety of data science languages that are perfect for these machine learning models.

 

1. Open source:

 

There are several open-source libraries available for building a topic modeling solution from the ground up. These are wonderful because they allow for customization and provide you total control over the whole process, from data pre-processing (tokenization, stopword removal, stemming, lemmatization, and so on) through feature extraction and model training (choosing the algorithm and its parameters). 

 

Python and R have some of the best libraries for topic modeling, and both of them are open-source programming languages.

 

2. SaaS APIs:

 

Machine learning is now available as a service, making it easier to use and requiring no programming knowledge. That's true, instead of writing your APIs and algorithms, all you have to do is utilize a user-friendly interface to create your machine learning service using your current data.

 

To use one of the APIs provided by machine learning services, you'll need to combine them with SDKs (Software Development Kits) for common programming languages or third-party integrations. Aside from it, everything else goes without a hitch. MonkeyLearn, Amazon comprehend, IBM Watson, Google cloud NLP, and other ML services are free to test.

 

Applications of Topic Modeling:

 

Some applications of topic modeling include:

 

• In graph-based models, topic modeling may be used to establish semantic relationships between words.

• It may be used to rapidly find out what the document or book is about via text summary.

• It can be used in exam scoring to eliminate prejudice against candidates. It also helps students acquire their findings promptly and saves a lot of time.

• It can enhance customer service by detecting the keyword in the client's question and responding appropriately. Customers will have more faith in you since you provided them with the assistance they required at the correct moment and without causing them any hassle. Customer loyalty improves dramatically as a result, and the company's worth rises as a result.

• It can recognize search terms and provide product recommendations to clients.

This has been a detailed blog on topic modeling wherein we discussed several aspects of this machine learning technique and how we can use it in real life.