This article will give you tips on how to analyze responses from a citizen survey about street cleanliness using the best AI survey response analysis approaches.
Choosing the right tools for analysis
The way you analyze survey responses depends on the data you collect. Quantitative data can be counted easily, but qualitative feedback from open-ended questions requires a more flexible approach.
Quantitative data: If you’re tracking things like “What percentage of citizens said streets are litter-free?”, you can simply tally responses in Excel or Google Sheets. This is straightforward and is best for checkbox or multiple-choice questions where patterns are easy to spot.
Qualitative data: These are open-text answers or detailed feedback from citizens about specific locations or issues. When hundreds of citizens share their thoughts—especially in a topic as nuanced as street cleanliness—manually reading each answer gets overwhelming. Here, AI tools help you find themes and gain clarity at scale.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
You can copy exported survey data into ChatGPT and chat about it. This sometimes works if you have a small dataset, but pasting large numbers of citizen responses can be cumbersome and exceeds the system’s input limits. There’s no structure to manage, segment, or revisit insights comfortably.
It’s not very convenient for analyzing dozens or hundreds of open-text survey responses, especially if you want to run follow-up questions, filter groups, or collaborate with a team. Manual effort grows quickly, and it’s far from seamless.
All-in-one tool like Specific
Specific is designed for qualitative survey analysis—including citizen feedback about street cleanliness—from start to finish. It collects responses using AI-driven conversational surveys and immediately summarizes open text, finds common themes, and distills data into insights with no spreadsheet or manual labor. One standout: it asks citizens for clarifications and follow-ups in real time, which boosts both data quality and the depth of insights.
With Specific’s AI survey response analysis, you can chat directly with AI about the results as you would in ChatGPT. You can also set filters, manage access, and segment what’s sent to the AI. It makes citizen feedback on street cleanliness manageable and actionable for local governments, NGOs, or municipal teams. See how this works in detail at Specific’s AI-powered response analysis page.
If you want to get started from scratch, try the AI survey generator for citizen street cleanliness surveys.
Useful prompts that you can use to analyze citizen responses about street cleanliness
One of the best ways to get real value from your citizen street cleanliness survey data is by using well-crafted prompts with your AI tool—whether you’re chatting in ChatGPT or with Specific itself.
Prompt for core ideas: Use this to quickly extract the main themes from open citizen responses. It’s designed to work on big sets of feedback—the same prompt powers Specific itself:
Your task is to extract core ideas in bold (4-5 words per core idea) + up to 2 sentence long explainer.
Output requirements:
- Avoid unnecessary details
- Specify how many people mentioned specific core idea (use numbers, not words), most mentioned on top
- no suggestions
- no indications
Example output:
1. **Core idea text:** explainer text
2. **Core idea text:** explainer text
3. **Core idea text:** explainer text
AI gives more valuable results if you provide context. Add key details about your survey’s goal (for example, “This survey about street cleanliness targets citizens in Mumbai. We want to know pain points and ideas for keeping streets clean.”):
Analyze responses from citizens about street cleanliness in our city. The survey aimed to identify areas that need improvement and understand what motivates people to keep streets clean.
You can also ask for deeper dives: "Tell me more about XYZ (core idea)"
Prompt for specific topic: When you suspect a problem or want to validate an idea, use: Did anyone talk about illegal dumping? Include quotes.
Prompt for pain points and challenges: Try this to get to the heart of what bothers citizens: Analyze the survey responses and list the most common pain points, frustrations, or challenges mentioned. Summarize each, and note any patterns or frequency of occurrence.
Prompt for sentiment analysis: Understand the mood of your citizens: Assess the overall sentiment expressed in the survey responses (e.g., positive, negative, neutral). Highlight key phrases or feedback that contribute to each sentiment category.
Prompt for suggestions and ideas: When crowd-sourcing solutions: Identify and list all suggestions, ideas, or requests provided by survey participants. Organize them by topic or frequency, and include direct quotes where relevant.
You’ll find more practical prompt templates in our guide on top citizen street cleanliness survey questions.
How AI analyzes qualitative data by question type in Specific
AI-powered analysis must adapt to a variety of survey question styles—something that stands out in conversational survey tools like Specific:
Open-ended questions (with or without follow-ups): The AI automatically summarizes all citizen feedback, plus the context from any dynamic clarifying questions. You get a digestible list of key themes with supporting quotes and stats.
Multiple-choice with follow-ups: Each choice gets its own summary of the follow-up responses, helping you see the “why” behind each selection. For instance, if citizens who select “streets are dirty” are probed further, you’ll have a breakdown of what specific issues contributed—litter, lack of bins, infrequent cleaning, etc.
NPS (Net Promoter Score): The AI groups reactions from detractors, passives, and promoters. Each group is summarized separately, so you know what makes happy citizens happy, and what frustrates those who score you poorly.
You can mimic some of this by copying sets of responses into ChatGPT and applying prompts manually. But it quickly becomes labor-intensive, especially as surveys get more sophisticated or you want to triangulate insights across segments.
To see what this looks like in practice—or to create your own structure—read our how-to on building citizen street cleanliness surveys or explore the AI survey editor.
How to tackle context size limits in AI survey analysis
GPT-based AIs have a context limit—the amount of text they can “see” at once. If you’ve collected hundreds (or thousands) of citizen comments, you’ll eventually hit this boundary. Specific bakes in two handy strategies:
Filtering: You can filter responses based on specific answers or questions. For example: only analyze citizens who reported dissatisfaction or mentioned a particular street. This narrows down the dataset to what’s most relevant and keeps you under the limit.
Cropping: Only send certain questions or responses to the AI for analysis. If you’re only interested in feedback on “public bins”, crop out everything else. This lets more conversations fit within the context window.
Both methods ensure you can analyze bigger, messier data sets—no need to worry about system errors or data loss. Specific handles this out of the box, but the principle is the same in any advanced AI-based survey tool.
Collaborative features for analyzing citizen survey responses
Analyzing citizen feedback on street cleanliness is rarely a solo act—teams, city officials, and local organizations often need to work together.
Analyze by chatting with AI. In Specific, anyone on your team can ask questions about the data—no technical expertise needed.
Multiple chat threads with custom filters. Create new chats focused on specific neighborhoods, types of feedback, or citizen groups. Each chat can use its own filters (like “only comments about bins in downtown”), keeping different lines of analysis organized. The system tracks who started each chat, so you always know whose angle you’re following.
Team visibility and transparent discussion. When collaborating, each AI chat message clearly shows who sent it, thanks to avatars. This makes it easier for city managers, researchers, and external consultants to coordinate insights and share learnings without confusion.
Curious what this looks like? Try out an interactive AI-powered street cleanliness survey demo for citizens or set up your own with this AI survey generator.
Create your citizen survey about street cleanliness now
Act quickly to uncover real citizen concerns and solutions for cleaner streets—get deep insights, instant summaries, and easy team collaboration out of the box.