Topic Modeling
Have you ever been in a situation where you were asked to find a specific piece of information for your company, from an extensive collection of documents, within a tight deadline?
When you are looking through the company database manually for a crucial piece of information, it is highly time-consuming and practically impossible.
With the growing amount of data in recent years, it is difficult to obtain the relevant and desired information in a short period, especially during urgent matters.
In such cases, we can use Topic Modeling to mine through the data and fetch the information we are looking for quickly.
It automatically identifies topics present in a text object and derives hidden patterns exhibited by a text corpus. Thus, assisting better decision making.
A good topic model should result in the following–
“health”, “doctor”, “patient”, “hospital” for a topic such as “Healthcare”.
For a business that deals with thousands of customer interactions daily, such as social media, chats, emails, open-ended survey responses, and so much more, it is almost impossible to conduct data analysis for each text. More so, with documents that require regular scanning for specific words and phrases.
From building brand awareness, sales, and marketing, improving customer experience to bioinformatics, Topic Modeling offers endless possibilities across different industries and areas within a company.
- What is Topic Modeling?
- Two types of Topic Modeling Algorithms?
- How Does Topic Modeling Work?
- Why Topic Modeling?
1. What is Topic Modeling?
Topic Modeling involves a statistical model that extracts abstract topics from your text based on the frequency of the particular terms used. It is a method in natural language processing (NLP) used to instruct machine learning models.
We may also refer to Topic Modeling as the process of logically selecting words that belong to a specific topic from within a document.
At Textrics, we optimize this process for you through our avant-garde technology. Latent Dirichlet Allocation is the most popular topic modeling technique. From a given dataset of documents, LDA backtracks and tries to figure out what topics would create those documents in the first place.
Topic Modeling helps businesses become more efficient by saving time on repetitive manual tasks and gathers insights from the text data they manage daily.
For example, a company that wants to identify areas to upgrade can run a survey asking users to rate their services and explain each rating. Topic modeling can accelerate this analysis by categorizing information into a topic like “most common reasons for low ratings.”
2. Two types of Topic Modeling Algorithms?
There are several algorithms for doing topic modeling. The most popular ones include:
- Latent Semantic Analysis(LSA)
Latent Semantic Analysis, or LSA, is one of the crucial foundation techniques in topic modeling. We can use it for text summarization, text classification, and dimension reduction. It is similar to the cosine similarity. As for LSA, we develop a matrix using the words present in the document’s paragraphs in the corpus. The matrix rows will represent the unique words present in each section, and columns represent each paragraph.
- Latent Dirichlet Allocation (LDA)
The Latent Dirichlet Allocation (LDA) & LSA are based on the same underlying assumptions: the distributional hypothesis, (i.e. similar topic makes use of similar words) and the statistical mixture hypothesis (i.e. documents talks about several topics) for which a statistical distribution can be determined.
The motive of LDA is to map each document in our corpus to a set of topics that covers a good deal of the words in the document.
3. How Does Topic Modeling Work?
Topic Modeling involves counting words and grouping similar word patterns to infer topics within unstructured data. So let’s say you're a software company and want to know what customers are saying about particular features of your product. So instead of spending hours & hours going through heaps of feedback, in an attempt to conclude which texts are talking about your topics of interest, you could analyze them with a topic modeling algorithm.
By detecting the patterns like word frequency and distance between words, a topic model clusters feedback that is similar. This also applies to words, phrases, and expressions that appear most frequently. And, with this information, you can instantly deduce what each set of texts are talking about.
4. Why topic modeling?
As large amounts of data are collected every day, more and more information becomes available. At the same time, it becomes difficult to access the necessary information that we are looking for.
Moreover, as these kinds of data are unstructured or free-form text, analyzing such volumes of text data manually becomes highly tedious and time-consuming.
The simple solution is to use Topic modeling, as it provides us with methods for automatically organizing, understanding, searching, and summarising extensive electronic archives.
It can help us sort through unstructured data in the following ways:
- Discovering the hidden themes in the collection.
- Classifying the documents into the discovered themes.
- Using the classification to organize/summarise/search the documents.
With the use of Topic Modeling, we can figure out what topics a bunch of unstructured text cover. This set of text documents may range from emails to survey responses, support tickets, product reviews, etc. Once we identify the topics, we can easily group them accordingly.
For example, a document belongs to the topics dogs, food, and health. And if a user queries for “dog food”, they might find this document relevant because it covers those topics (among others).
Therefore, we can figure its relevance for the query without even going through the whole document. By annotating the document based on the topics predicted by the modeling method, we can optimize our search process.
With the help of Textrics, you can organize, search and understand large quantities of information very quickly, no matter which platform they originate from.
Request a free demo right away. For more help, contatct our team of experts who can guide you every step of the way.
Further Reading → Know the objective of the text generated by user throguh Intent Analysis.