Zencity’s Dynamic Topic Model

Zencity’s new Dynamic Topic Model takes thematic tagging of open-ended survey responses to the next level, enabling more accurate, nuanced categorization of open-text responses. Unlike traditional topic modeling, which applies a fixed set of generic topics that may not reflect the unique characteristics of a local community and are rarely updated, the Dynamic Topic Model identifies highly specific topics tailored to each community and continuously adapts by adding new ones as they emerge. Utilizing advanced natural language processing (NLP) techniques, the model analyzes the semantic meaning of survey responses, assigning them to one or more relevant topics and subtopics based on the collected answers for each question. This ensures your survey data is labeled with exceptional precision, uncovering deeper trends and insights from resident comments.

How Does the Dynamic Labeling Process Work?

When a survey response is submitted, it is analyzed and categorized using Zencity’s dynamic topic and subtopic tree. This process follows these steps:

1. Each response is carefully analyzed and broken down into keywords and phrases that reflect its meaning.
2. The analyzed responses are used to identify the most frequently mentioned topics within each community. These topics may include broad themes, such as housing or law enforcement, and specific local issues, like an elected official, a policy change, or a community event.
3. The identified topics are structured into a clear set of labels, which are then applied to tag individual responses that align with each topic.
4. The system is periodically updated to incorporate new and emerging topics within each community, ensuring both new and past responses are labeled with the most relevant and up-to-date information.

The model requires a dataset of at least 400 valid, labelable responses to start labeling responses. When your survey is launched, or a new open-text question is added, there will be an initial training period before the model generates topics and starts labeling responses. Once this threshold is reached, the model labels all previously collected data and begins analyzing new responses as they come in.

Ensuring Accurate and Relevant Topics

To maintain precision and relevance, Zencity combines automated processes with human expertise:

• Human Annotation: Skilled professionals review and annotate sample responses to teach the model to recognize patterns and themes more effectively. This process plays a key role in improving the model’s accuracy.
• Manual Adjustments: While the model generates topics automatically, expert reviews are conducted to refine and adjust clustering results when needed. This ensures the highest possible quality for labeled data.
• Iterative Refinement: The model is continuously retrained using new data, ensuring that topics stay accurate and align with evolving trends. Feedback loops and periodic updates improve its performance over time. This process is still a work in progress, with ongoing efforts to refine and optimize the approach.

By combining these efforts, we ensure your insights remain reliable and actionable. 

Key Benefits of the Model 

• Tailored to Your Community: The Dynamic Topic Model is designed to reflect the unique characteristics of each community. For example, the same survey question might generate different categories in different cities based on the local context and priorities.
• Enhanced Precision: The model captures nuanced insights that might be missed with traditional approaches by analyzing semantic meaning rather than relying on static keywords.
• Multi-Label Assignment: Each response can be assigned multiple labels, allowing for a more comprehensive set of topics and ensuring detailed responses are linked to all relevant topics.
• Customizability: The model adapts to your community's unique needs and evolves alongside your data. It can identify connections between topics, such as linking a Police Chief's name to their role, ensuring that responses mentioning them by name are associated with broader themes like policing.

Where Can I Find the Model’s Output in my Dashboard

The output of the Zencity Topics Model, which focuses on text-based feedback, is available in the “Feed” tab of your dashboard. Within this section, you can explore individual comments from open-text questions, each categorized into relevant topics and subtopics. For clarity, each comment is labeled with its assigned subtopics. Additionally, a summary of the topics and subtopics is conveniently displayed on the right side of the comment feed.

FAQs

How often is the model updated for improvement purposes? 

The model is periodically updated and retrained to enhance accuracy and insights. Updates might recalibrate historical data to ensure all results reflect the latest advancements

What happens to responses when the model is updated?

When updates are made, new labels are added to expand and enhance the model, while all existing labels remain unchanged. Responses are re-analyzed to incorporate the newly added labels, ensuring that historical data is aligned with the updated tagging logic without altering the original labeling framework.

Can responses have more than one label?

Yes! Responses can be assigned multiple subtopics, providing a richer understanding of the themes within your data.

How often is data enriched with labels?

We label incoming responses to all surveys three times a day. 

How are percentages for each topic calculated?

Percentages for each topic are calculated based only on labeled responses. Unlabeled responses that don’t fit into any topic are excluded. A topic's percentage is the number of responses labeled under it divided by the total labeled responses. These percentages, shown in dashboards and reports, provide users with a clear picture of how open-text responses are distributed across topics. Since unlabeled responses are excluded, the percentages represent the distribution of labeled responses only, ensuring that the data is focused on meaningful and categorized insights.