Why Your Business Needs Dynamic Topic Modeling Today: From Data to Decisions
Unlock actionable insights and stay ahead of the competition with this cutting-edge AI tool that tracks evolving trends in customer and market data.
Recently, I have been researching lesser-known topics in Data Science. You know, most people in data science don’t talk about them. It could be that most people need to be made aware of it or that it is a challenging topic to learn and explain to others. So that lit a bulb in me. I can look up complex issues that need to be covered.
It may be the researcher in me that never came to fruition entirely. For context, I started a PhD in Data Science program but could not go past two semesters because my interests changed midway. I am more interested in the practical application of data science, and being fully vested in a PhD program meant that I would have to dedicate my week and life to it entirely for the next 4–5 years.
One of the many lesser-known topics that interested me was Dynamic Topic Modeling. Most of us know the concept of Topic Modeling, and if you need a refresher, you can see this explanation. However, only a few of us, including myself, were familiar with Dynamic Topic Modeling.
I took it upon myself to help teach tech-savvy and non-tech readers. If you, as a data scientist, were to interact with your colleagues in finance or marketing, you should be able to explain what it is and why you used it to solve their problem. Well, Most people in business don’t care about how a problem is solved. It doesn’t matter to them if you use fancy tech like AI or Crypto as long as their problem is solved.
Let’s assume your colleague is more interested in the tech piece for a second and is looking for an easy explanation so that we all can move on to the next problem (Because running a business solves one problem after another, haha!).
Dynamic Topic Modeling (DTM) is like the superpower of understanding how themes in text evolve over time. For businesses, it provides a way to track the pulse of consumer sentiment or analyze trends in massive textual datasets.
In this blog, we’ll see how Jane, a data scientist, explains the technical details of DTM to John, a non-technical business leader, in a way that both makes sense and adds value. Jane dives deep into the tech jargon and then switches gears to make it all click for John.
Part 1: The Tech Jargon (Jane’s Perspective)
Jane (Techie): “Alright, John. Let me break down how Dynamic Topic Modeling works. It’s basically a probabilistic machine learning model used for analyzing textual data, especially when that data is distributed over time — like social media posts or annual reports.”
How It Works
1] Foundation in Probabilistic Modeling: DTM builds on static topic models, such as Latent Dirichlet Allocation (LDA), but introduces a temporal dimension. Think of it as tracking the evolution of topics across multiple time points.
Topics are clusters of words that frequently occur together in the data.
Each topic is represented as a probability distribution over words, and each document is a probability distribution over topics.
2] The Magic of Temporal Dependency: “In static models, topics are fixed. DTM, on the other hand, assumes that topics evolve over time. Mathematically, it models the relationship between topics in consecutive time slices using Gaussian priors. This enables the model to ‘smooth’ the topic evolution.”
where θt\theta_t represents the topic distribution at time t.
3] Optimization Techniques: “We use techniques like variational inference or Gibbs sampling to train the model. These help approximate the posterior distributions since the direct computation of these probabilities is intractable for large datasets.”
Python Implementation
“Let me show you some code to implement this in Python using the gensim library and a precompiled DTM binary.”
from gensim.models.wrappers import DtmModel
from gensim.corpora import Dictionary# Example corpus
documents = [
["dynamic", "topic", "modeling", "data", "time"],
["probabilistic", "machine", "learning", "evolution"]
]
dictionary = Dictionary(documents)
corpus = [dictionary.doc2bow(doc) for doc in documents]# Define time slices (e.g., documents per year)
time_slices = [1, 1]# Load the DTM model binary
dtm_path = "/path/to/dtm-binary"
dtm_model = DtmModel(dtm_path, corpus=corpus, time_slices=time_slices, id2word=dictionary)# Get topics over time
topic_words_time1 = dtm_model.show_topic(topicid=0, time=0, topn=5)
topic_words_time2 = dtm_model.show_topic(topicid=0, time=1, topn=5)print(f"Time Slice 1: {topic_words_time1}")
print(f"Time Slice 2: {topic_words_time2}")Part 2: Simplifying for the Business Perspective (John’s Perspective)
John (Non-Tech): “Okay, Jane, I get that this is powerful, but you lost me at ‘Gaussian priors.’ How does this help the business?”
Jane: “Let’s step back and look at what this does for us in plain terms. Imagine we’re tracking customer reviews about our products over the past three years. Dynamic Topic Modeling analyzes all that data and shows us how customer sentiments and priorities change.”
Breaking It Down: A Practical Example
Jane: “Picture this scenario. In the 1st year, our customers are talking about the ‘design’ of our product. In the 2nd year, ‘durability’, and in the third year, they talk about ‘price’ and ‘value.’ DTM identifies these changes automatically, helping us adjust our messaging and product focus.”
John: “That’s very interesting and sounds exciting! So, it goes beyond what the customers say. It tracks how their focus is evolving?”
Jane: “Exactly. It is like a Back to the Future kind of time machine, but you can only travel to the past. So, only 1955, Haha! And only for customer feedback. Let’s take a concrete example from our Smart Tablet division:
In 2021, people raved about its sleek design.
In 2022, they highlighted battery life as a key feature.
In 2023, they started comparing our prices to competitors.
DTM tracks these shifts and allows us to make better decisions. For instance, we might increase ads about our affordability in 2023 because that’s what people care about.”
Other Use Cases
Competitor Analysis: “We can analyze public forums or social media to see what competitors’ customers are complaining about. If their smartwatch users are upset about battery life, we emphasize ours in ads.”
Trend Forecasting: “We can identify emerging trends before they become mainstream. Imagine seeing a gradual rise in mentions of ‘sustainability’ — we could launch eco-friendly packaging ahead of competitors.”
Internal Employee Feedback: “DTM works internally too. If employee sentiment shifts from ‘growth opportunities’ to ‘work-life balance,’ we know what to focus on during our Human Resource initiatives.”
Turning Complexity into Strategy
John: “But what if I don’t care about the technical details? I just want the insights.”
Jane: “That’s the beauty of it. You don’t have to deal with the code. DTM produces visualizations and reports that are easy to understand. For instance, you’ll see graphs showing which topics were most discussed each year.”
Imagine this simplified chart:
Year| Top Words| Key Insight
2021| sleek design| Focus on design in ads.
2022| battery, life| Highlight long battery life.
2023| price, value| Push affordability campaigns.
John: “So, it’s like having customer trends in the palm of your hand but not going through every single review because that is super time-consuming, right?”
Jane: “Exactly! It’s a shortcut to understanding your audience over time.”
Part 3: Why It Matters for Businesses
Let’s look at why it matters for Businesses like ours. Dynamic Topic Modeling isn’t just a techie like myself’s playground. It is a strategic tool for staying ahead. For businesses, here’s why DTM matters:
Stay Relevant: Know what our customers care about most right now.
Predict Trends: Spot shifts in sentiment before our competitors do.
Enhance Communication: Align our messaging with our customers’ priorities.
Final Words from Jane, The Data Scientist:
Jane: “DTM is like having a magical crystal ball that analyzes our data and tells us where we need to focus our business strategy. Whether that is looking at our consumer’s trends, analyzing our competitors, or internal employee sentiment, it is the ultimate tool for turning text data into actionable insights.”
John: “Love it. Let’s get this implemented ASAP.”
Jane: “Sure thing, John. I’ll set up the pipeline, and you’ll have your insights before the next strategy meeting!”
Conclusion
So, you can see how Dynamic Topic Modeling helps close the gap between complex programs (complex algorithms) and actionable business strategies. Whether you identify yourself as a Techie Jane who dives into mathematics and code or like a Business user, John, who is focused on decision-making, DTM is a tool that delivers value to all stakeholders.
Don't let your business fall behind! Discover how Dynamic Topic Modeling transforms complex data into strategic decisions and helps you predict trends, enhance communication, and stay relevant in a rapidly changing market.