Weaving Fashion Trends through Topic Modeling and Social Media Insights

Social media isn’t just about sharing updates or following influencers—it’s a constant flow of conversations reflecting what’s happening worldwide. Every day, platforms like Twitter and Instagram become the battlegrounds where trends, opinions, and ideas take off or fizzle out. But for those who can cut through the noise, there’s a treasure trove of insights waiting to be discovered.

At ChicLytic, I merge fashion analysis with data science to track these conversations and uncover real-time insights into emerging fashion trends. By analyzing social media data, I predict which trends are on the rise and which are fading, helping fashion professionals and enthusiasts stay ahead of the curve. For the latest trend breakdowns and data-driven fashion insights, follow @ChicLytic on Instagram or visit the project page at data.wila.me/chiclytic.

The real value lies in tracking trends as they emerge. Identifying these patterns early can make a huge difference, whether it’s the latest fashion craze, a shift in consumer sentiment, or something entirely unexpected. This is where data mining and analysis come in.

Topic Modeling and LDA: An Overview

Before we dive into specifics, it’s important to explain what topic modeling is and how LDA (Latent Dirichlet Allocation) fits into the picture. Topic modeling is a Natural Language Processing (NLP) technique used to discover hidden themes or “topics” within large volumes of text. When working with vast social media posts, it’s impossible to manually sift through every message to understand what people are talking about. That’s where topic modeling comes in—it groups similar discussions, revealing patterns that may not be immediately obvious.

LDA is a popular method for topic modeling. It’s a probabilistic model that assumes each document (or social media post) is a mixture of topics, representing each topic as a word distribution. LDA uncovers hidden thematic structures by analyzing word co-occurrences, making it easier to spot emerging trends.

For example, after analyzing a dataset, LDA might reveal a topic like “comfy fall, business casual, skinny jeans, and 18th century,” suggesting discussions around seasonal fashion, casual workwear, and nostalgic influences. Although algorithmically generated, these topics help us identify broader patterns across social media conversations.

However, it’s important to remember that LDA doesn’t provide perfectly cohesive topics. Some terms may seem disconnected, and to fully understand what these topics represent, it’s necessary to examine them in context with other generated topics.

Mining Social Media Posts

The first step in discovering the underlying topics of social posts is gathering data. Social media is an endless stream of content, so capturing the right information efficiently and securely is essential. I rely on APIs to collect the data I need. APIs offer a safer, more compliant way to access social media platforms compared to web scraping, which often violates terms of service and can lead to blocking or legal issues. I use APIs to ensure that I operate within platform regulations, making the data collection process legal and stable.

Preprocessing: Cleaning and Organizing Text Data

Once the data is collected, it needs to be preprocessed. Social media posts are messy—noisy, with inconsistent spelling and informal language. Preprocessing helps clean up this noise, ensuring the data is ready for LDA.

For my analysis, I take a targeted approach. While others might remove emojis or hashtags, I keep them. These elements often carry important context, especially in fashion conversations, where a hashtag or emoji can symbolize trends or moods. However, I do remove things like URLs and perform text normalization to standardize words (e.g., “shirt” and “shirts” becoming “shirt”). This reduces variation and ensures the model recognizes these as the same word.

I also pay special attention to stopwords. Traditional stopwords (e.g., “the,” “and”) are often removed from text data, but in fashion, certain frequent words like “style” or “look” are essential to understanding the conversation. I retain these field-specific stopwords to ensure the analysis captures the full context of discussions.

Once cleaned, the text is tokenized (broken down into individual words), and the dataset is structured so that LDA can analyze it.

The Role of Statistical Analysis in Refining Data

Before applying LDA, I do some statistical analysis to refine the dataset further. Specifically, I analyze the frequency of term occurrences over time to track the rise or fall of specific discussions. Additionally, I monitor how many documents (social media posts) are created daily to detect spikes in activity around particular topics. This allows me to ensure that I work with a focused, high-quality dataset before applying LDA.

Grouping Topics: Positive vs. Negative Trends

After LDA identifies the key topics, the next step is determining whether these topics reflect positive, negative, or neutral trends. For sentiment analysis, I rely on transformer-based models like BERT and RoBERTa. In my experience, transformers perform much better than traditional sentiment analysis models because they capture nuances in the conversation, such as sarcasm, trend-specific language, and layered expressions.

For example, when analyzing a topic like “low-rise jeans,” a transformer doesn’t just look at individual words like “love” or “hate.” Instead, it interprets how these words interact within a sentence, helping to determine whether people are genuinely excited about the trend or expressing negative opinions. This approach provides a more accurate and context-sensitive sentiment score.

Once the sentiment is classified, I group the topics into positive, negative, or neutral trends. This gives me a more nuanced understanding of the fashion landscape, helping me determine which trends are rising, controversial, or fading. This approach allows me to identify people's feelings about certain topics, leading to more insightful, actionable conclusions.

Using Streamlit for Visualization

I use a Streamlit dashboard to visualize the LDA-identified topics and their associated sentiments to make these insights actionable. Bar charts help me show the frequency of different topics, while word clouds highlight the most common terms within each topic. I also use line charts to track trends over time and tables to present structured insights for more in-depth analysis.

These visualizations make it easy to identify which topics are gaining momentum, which are declining, and how sentiment is shifting around specific trends.

The Value of Data-Driven Insights

I uncover valuable insights from vast social media conversations by combining LDA for topic modeling with transformer-based sentiment analysis. This process allows me to break down chaotic text streams into actionable trends, helping me and my audience spot what’s rising or fading in fashion.

It is crucial to spot emerging trends before they take off, especially in fast-moving industries like fashion. Understanding these trends early can lead to more effective marketing strategies and staying ahead of the competition.

While this process already yields significant insights, I’m continuously seeking improvement. One area of focus is using large language models (LLMs) to enhance topic interpretation further. LLMs can help generate clearer, more understandable clusters of information by offering deeper insights into the context of discussions. While LDA provides a solid foundation, LLMs like GPT can assist in summarizing and refining topic clusters to make them more user-friendly and actionable.