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I’m sure you’ve heard of artificial intelligence, as well as words like machine learning and natural language processing (NLP).
Especially if you work for a firm that handles hundreds, if not thousands, of client contacts every day.
Data analysis of social media postings, emails, chats, open-ended survey replies, and other sources is not a simple process, and it becomes even more difficult when entrusted only to people.
That is why many people are enthusiastic about the potential of artificial intelligence for their day-to-day work and for enterprises .
AI-powered text analysis employs a broad range of approaches or algorithms to interpret language organically, one of which is topic analysis, which is used to automatically discover subjects from texts.
Businesses can use topic analysis models to transfer easy jobs onto machines rather than overburden workers with too much data.
Consider how much time your team might save and devote to more essential work if a computer could filter through endless lists of customer surveys or support issues every morning.
In this guide, we’ll look into topic modeling, different methods of topic modeling, and get some hands-on experience with it.
What is Topic Modeling?
Topic modeling is a type of text mining in which unsupervised and supervised statistical machine learning techniques are used to detect trends in a corpus or a significant volume of unstructured text.
It can take your massive collection of documents and use a similarity method to arrange the words into clusters of terms and discover subjects.
That seems a little complex and hard, so let’s simplify the subject modeling procedure!
Assume you’re reading a newspaper with a set of colored highlighters in your hand.
Isn’t that old-fashioned?
I realize that these days, few people read newspapers in print; everything is digital, and highlighters are a thing of the past! Pretend to be your father or mother!
So, when you read the newspaper, you highlight the important terms.
One more assumption!
You use a different hue to emphasize the keywords of various themes. You categorize the keywords depending on the provided color and topics.
Each collection of words marked by a certain color is a list of keywords for a given topic. The amount of various colors you picked shows the number of themes.
This is the most fundamental topic modeling. It aids in the comprehension, organization, and summarization of large text collections.
However, keep in mind that to be effective, automated topic models require a lot of content. If you have a short paper, you might want to go old school and use highlighters!
It’s also beneficial to spend some time getting to know the data. This will give you a basic sense of what the topic model should find.
For instance, that diary may be about your present and previous relationships. Thus, I’d anticipate my text mining robot-buddy to come up with similar ideas.
This can help you better analyze the quality of the subjects you’ve identified and, if necessary, tweak the keyword sets.
Components of Topic Modeling
Random variables and probability distributions are incorporated into the representation of an event or phenomenon in probabilistic models.
A deterministic model provides a single potential conclusion for an event, whereas a probabilistic model provides a probability distribution as a solution.
These models consider the reality that we rarely have complete knowledge of a situation. There is almost always an element of randomness to consider.
For example, life insurance is predicated on the reality that we know we will die, but we don’t know when. These models might be partially deterministic, partially random, or entirely random.
Information retrieval (IR) is a software program that organizes, stores, retrieves, and evaluates information from document repositories, particularly textual information.
The technology helps users discover the information they need, but it does not clearly deliver the answers to their inquiries. It notifies of the presence and location of papers that may provide the necessary information.
Relevant documents are those that meet the needs of the user. A faultless IR system will return only selected documents.
Topic Coherence scores a single topic by calculating the degree of semantic similarity between the topic’s high-scoring terms. These metrics aid in distinguishing between subjects that are semantically interpretable and topics that are statistical inference artifacts.
If a group of claims or facts supports each other, they are said to be coherent.
As a result, a cohesive fact set can be understood in a context that encompasses all or the majority of the facts. “The game is a team sport,” “the game is played with a ball,” and “the game requires tremendous physical effort” are all examples of cohesive fact sets.
Different Methods of Topic Modelling
This critical procedure can be carried out by a variety of algorithms or methodologies. Among them are:
- Latent Dirichlet Allocation (LDA)
- Non Negative Matrix Factorization (NMF)
- Latent Semantic Analysis (LSA)
- Probabilistic Latent Semantic Analysis(pLSA)
Latent Dirichlet Allocation(LDA)
To detect relationships between multiple texts in a corpus, the statistical and graphical concept of Latent Dirichlet Allocation is used.
Using the Variational Exception Maximization (VEM) approach, the largest likelihood estimate from the full corpus of text is achieved.
Traditionally, the top few words from a bag of words are chosen.
However, the sentence is completely meaningless.
According to this technique, each text will be represented by a probabilistic distribution of subjects, and each topic by a probabilistic distribution of words.
Non Negative Matrix Factorization(NMF)
Matrix with Non-Negative Values Factorization is a cutting-edge feature extraction approach.
When there are many qualities and the attributes are vague or have poor predictability, NMF is beneficial. NMF can generate significant patterns, subjects, or themes by combining characteristics.
NMF generates each feature as a linear combination of the original attribute set.
Each feature contains a set of coefficients that represent the importance of each attribute on the feature. Each numerical attribute and each value of each category attribute has its own coefficient.
All of the coefficients are positive.
Latent Semantic Analysis
It is another unsupervised learning method used to extract associations between words in a set of documents is latent semantic analysis.
This helps us to choose the proper documents. Its primary function is to reduce the dimensionality of the enormous corpus of text data.
These unnecessary data serve as background noise in acquiring the necessary insights from the data.
Probabilistic Latent Semantic Analysis(pLSA)
Probabilistic latent semantic analysis (PLSA), sometimes known as probabilistic latent semantic indexing (PLSI, notably in information retrieval circles), is a statistical approach for analyzing two-mode and co-occurrence data.
In fact, similar to latent semantic analysis, from which PLSA emerged, a low-dimensional representation of the observed variables can be derived in terms of their affinity to particular hidden variables.
Hands-on with Topic Modeling in Python
Now, I’ll walk you through a subject modeling assignment with the Python programming language using a real-world example.
I’ll be modeling research articles. The dataset I’ll be using here comes from kaggle.com. You can easily obtain all of the files that I am using in this work from this page.
Let’s get started with Topic Modeling using Python by importing all of the essential libraries:
The following step is to read all of the datasets that I will be using in this task:
Exploratory Data Analysis
EDA (Exploratory Data Analysis) is a statistical method that employs visual elements. It uses statistical summaries and graphical representations to discover trends, patterns, and test assumptions.
I’ll do some exploratory data analysis before I start topic modeling to see if there are any patterns or relationships in the data:
Now we will find the null values of the test dataset:
Now I will be plotting a histogram and boxplot to check the relation between the variables.
The amount of characters in the Abstracts of the Train set varies greatly.
On the train, we have a minimum of 54 and a maximum of 4551 characters. 1065 is the average amount of characters.
The test set looks to be more interesting than the training set since the test set has 46 characters while the training set has 2841.
As a result, the test set had a median of 1058 characters, which is similar to the training set.
The number of words in the learning set follows a similar pattern to the number of letters.
A minimum of 8 words and a maximum of 665 words are allowed. As a result, the median word count is 153.
A minimum of seven words in an abstract and a maximum of 452 words in the test set are required.
The median, in this case, is 153, which is identical to the median in the training set.
Using Tags for Topic Modeling
There are several topic modeling strategies. I’ll use tags in this exercise; let’s look at how to do so by examining the tags:
Applications of Topic Modeling
- A text summary can be used to discern the topic of a document or book .
- It can be used to remove candidate bias from exam scoring.
- Topic modeling might be used to build semantic relationships between words in graph-based models.
- It can enhance customer service by detecting and responding to keywords in the client’s inquiry. Customers will have more faith in you since you have provided them with the assistance they require at the appropriate moment and without causing them any hassle. As a result, client loyalty rises dramatically, and the company’s worth increases.
Topic modeling is a sort of statistical modeling used to uncover abstract “subjects” that exist in a collection of texts.
It is a form of the statistical model used in machine learning and natural language processing to uncover abstract concepts that exist in a set of texts.
It is a text mining method that is widely used to find latent semantic patterns in body text.