This article will give you tips on how to analyze responses from a citizen survey about mental health support awareness using AI and other survey response analysis strategies.
Choosing the right tools for analysis
The approach and tools you choose for analyzing survey data depend on the structure of your responses.
Quantitative data: Numbers are your friend here. When you want to count how many people chose a specific option or measure Net Promoter Score (NPS), conventional tools like Excel or Google Sheets make calculation fast and straightforward.
Qualitative data: Things get trickier with open-ended responses or follow-up comments. With dozens, sometimes hundreds of answers, it’s impossible to read everything in detail. This is where AI tools shine—they help you make sense of text-heavy responses efficiently.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
The simplest way to get started with AI analysis is copying your exported responses into ChatGPT, Gemini, or Claude.
You can ask questions about your data and get instant summaries or insights. However, this method can be clunky. You have to manage data exports, only copy-paste what fits the AI’s context limit, and keep track of which responses you’re analyzing—it’s easy to get lost.
Handling data this way isn’t very convenient for larger surveys, and collaboration is limited, as ChatGPT conversations are siloed to individual sessions and people.
All-in-one tool like Specific
Specific is purpose-built for modern survey analysis—including conversational surveys with follow-up questions and rich qualitative feedback. It handles both survey creation and AI-powered survey response analysis. When you collect data, it can automatically generate smart follow-up questions that dig deeper, boosting the quality and relevance of every response.
The real magic of Specific is in how easily it turns qualitative feedback into actionable insights. Its AI engine instantly spots core themes, summarizes insights from large data sets, and even lets you chat with the analysis—just like ChatGPT, but purpose-built for survey data. Plus, you have extra powers, like managing context or filtering responses before chatting. For more, check out Specific’s guide to AI survey analysis.
Other analysis platforms exist—like NVivo, MAXQDA, and ATLAS.ti—with strong AI-assisted coding and collaboration features[8][9]. Still, I’ve found that all-in-one tools designed specifically for surveys (like Specific) offer a much quicker route for getting actionable answers from messy, open-ended data.
Useful prompts that you can use to analyze Mental Health Support Awareness citizen survey responses
Prompts turn AI into your personal research analyst. Here are some tried-and-true prompt ideas—tailored for citizen mental health support awareness surveys:
Core ideas prompt: Use this to quickly surface main topics or themes from your responses. This prompt is especially powerful if you have pages of open-ended comments or anecdotes.
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
Always provide context! The more background you give to the AI—like survey goals, your city or population type, or what you want to know—the better the results. For example:
You are analyzing survey data from 300 citizens in Harrisburg about awareness of mental health support services. Our goal is to understand what citizens know about mental health resources, common misconceptions, and potential barriers to accessing these services. Please summarize the main findings and identify gaps in public awareness.
Dive deeper into any key idea with a “tell me more” prompt:
Tell me more about barriers to accessing mental health support mentioned by respondents.
Prompts to validate specific topics or hypotheses: If you want to check if a topic was discussed, just ask:
Did anyone talk about awareness of the 988 suicide hotline? Include quotes.
Persona prompts: Useful for segmenting your citizen survey audience into groups (e.g., those with high awareness vs. low awareness).
Based on the survey responses, identify and describe a list of distinct personas—similar to how "personas" are used in product management. For each persona, summarize their key characteristics, motivations, goals, and any relevant quotes or patterns observed in the conversations.
Pain points and challenges: Pinpoint what’s holding people back, which directly informs what to work on in mental health support initiatives.
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.
Motivations & drivers: This prompt helps reveal why people do (or don’t) seek mental health support.
From the survey conversations, extract the primary motivations, desires, or reasons participants express for their behaviors or choices. Group similar motivations together and provide supporting evidence from the data.
Sentiment analysis: Great for getting a sense of overall mood or attitudes about local mental health support systems.
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.
Unmet needs & opportunities: Use this when you want to move from analysis to recommendations—what’s missing and what could help.
Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.
How Specific handles different question types in qualitative survey analysis
Specific automatically adapts its AI summaries depending on your question type:
Open-ended questions with or without follow-ups: You get a summary that draws on all initial answers and any associated follow-ups, giving a rich picture of every topic respondents brought up.
Choices with follow-ups: For every choice (for example, “Yes, I know about 988” or “No, I don’t”), you see distinct summaries for follow-up responses just from people who picked that choice. You immediately see what’s unique to each group.
NPS surveys: Feedback is split into summaries for each group—detractors, passives, and promoters—drilling down into their different views and suggestions.
If you prefer using ChatGPT, you can replicate most of this by filtering your data before pasting it into the chat, but it’s definitely more manual work.
Working around AI context limits when analyzing citizen surveys
When you have a large number of responses, many AI tools—including ChatGPT—run into context size limits. That means you can’t just copy and paste all results; something will get missed. Specific tackles this elegantly in two ways:
Filtering: Only want feedback from those who mentioned “988 hotline” or answered a given follow-up? Apply filters so the AI analyzes just those conversations, keeping things focused and within limits.
Cropping: You can restrict the AI’s attention to certain questions—say, only the “awareness” section or only comments about barriers—so it doesn’t waste space on irrelevant data.
This approach is especially important for citizen surveys about mental health, which may pull in hundreds of nuanced stories. AI can work up to 70% faster than manual methods and reach up to 90% accuracy in tasks like sentiment or theme analysis[10].
Collaborative features for analyzing citizen survey responses
Survey analysis can feel lonely or overwhelming when you’re the only one sifting through the data. But in real life, teams and stakeholders all want a say—especially for sensitive topics like mental health support in the community.
Specific lets you analyze data by chatting with the AI—collaboratively. Anyone on your team can open a fresh chat, apply their own filters or context, and look at the results from their perspective. Each chat thread shows the creator, so you know whose line of questioning you’re following. This keeps analysis transparent and helps teams crowdsource their understanding.
See who said what with avatars. Whenever colleagues contribute prompts or notes in a chat, you’ll see their avatars. This makes it easy to hand off incomplete analyses, pick up where others left off, and keep collaboration energized, regardless of your team’s size or background.
If you want to start your own survey analysis project from scratch or with proven templates, I recommend checking out Specific’s survey generator for mental health support awareness or learning how to create better citizen surveys step by step with this how-to guide.
Create your citizen survey about mental health support awareness now
Collect richer data, get instant AI-powered insights, and collaborate seamlessly—make citizen voices matter in your community’s approach to mental health support.