DTTP AI PM

AI-Enhanced Word Cloud for Mentimeter

Real-time AI transforms Word Cloud responses into meaningful, actionable insights.

Problem Space

Many educators, corporate trainers, and conference speakers use Mentimeter’s Word Cloud for real-time feedback, yet it lacks deeper text processing. This causes fragmented, unclear audience insights and requires manual interpretation.

Problem Statement

How might we enhance Mentimeter’s Word Cloud with AI to improve sentiment detection, word grouping, and multi-word phrase recognition so that presenters can extract clearer, more actionable insights?

Problem Background

  • Pandemic Shift (2020): More online presentations.
  • Mentimeter’s Popularity: Quick engagement tool, but no built-in text intelligence.
  • Limitations:
    1. No Sentiment Analysis
    2. Fragmented Synonyms
    3. Split Multi-Word Phrases

Research Insights

  1. 85% want real-time sentiment detection for immediate mood checks.
  2. 70% found repeated synonyms (e.g., “fun,” “funny”) confusing.
  3. Corporate trainers disliked large-volume manual sorting.

User Pain Points

  • Time-Consuming Interpretation: Presenters manually merge synonyms.
  • No Real-Time Sentiment: Hard to adjust content mid-session.
  • Fragmented Keywords: “excited,” “exciting,” and “excited!” scattered.

Feedback

  • Real-time sentiment analysis increases Mentimeter usage.
  • Auto grouping of synonyms and multi-word phrases saves time, clarifies major themes.

Landing on the Solution

Four Key Enhancements

  1. Real-Time Sentiment Analysis
  2. Automatic Grouping of Synonyms
  3. Multi-Word Phrase Recognition
  4. Customization/Moderation Controls

Explanation of Solution

  • Prototyping & Testing: Small user group reported 95% time-saving in manual grouping.
  • Sentiment Tags: Enables presenters to identify negative areas quickly and pivot session content.

User Flows / Mockups (Optional)

  1. Presenter sets up a Word Cloud question in Mentimeter.
  2. Audience submits open-ended responses.
  3. AI merges synonyms, detects sentiment, and highlights multi-word phrases.
  4. Presenter may override any incorrect AI classification.
  5. Enhanced Word Cloud updates in real time.

Future Steps

  • Multilingual Support: Expand beyond English.
  • Advanced Analytics: Provide time-based sentiment changes and deeper theme analysis.
  • Integration: Extend AI to Mentimeter polls, quizzes, or Q&A features.

Images - Screenshots or Marketing Assets

https://www.figma.com/design/Hq7LhNO9iPhJzJIzTQP7Pz/Untitled?node-id=0-1&t=wuNwTFXF73nVgi5N-1 

 

 Additional Links

Learnings

Product Manager Learnings:

Sourabha Sourabha

Working on Co.Lab’s AI Product track offered a deep dive into user-centric design and data science fundamentals. I saw firsthand how user research validates assumptions around Word Cloud pain points and how iterative feedback loops guide AI model enhancements.

Key Challenges

  • Real-Time Performance vs. Accuracy: Maintaining sub-second latency with ~85% sentiment accuracy was non-trivial.
  • Data Quality & Edge Cases: Handling slang, abbreviations, or user typos required continuous model refinement.
  • Scoping the MVP: Balancing must-have features (sentiment detection, grouping) with nice-to-haves (advanced analytics).

Personal Growth

  • Learned the value of cross-functional collaboration with data scientists and developers.
  • Improved my communication skills, especially in explaining AI limitations and benefits to stakeholders.
  • Gained confidence in iterative product development, ensuring that real user feedback shapes each sprint.

Designer Learnings:

Designer Learnings:

Jo Sturdivant

  1. Adapting to an Established Team: Joining the team in week 6 of 8 was challenging, as I had to quickly adapt to existing workflows, dynamics, and goals. This mirrors real-world situations where you often integrate into teams mid-project, and flexibility is essential.
  2. Work-Blocking for Efficiency: With only two weeks to complete the project, I learned the importance of a structured work-blocking system. This approach allowed me to manage my time effectively and meet deadlines under pressure.
  3. Making Data-Driven Design Decisions: Unlike my past projects, I had to rely on research conducted by others. This was a valuable experience in using pre-existing data to guide design decisions, helping me focus on the core insights without starting from scratch.

Developer Learnings:

Developer Learnings:

Vanady Beard

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As the back-end developer, I learned how important it is to create efficient and reliable systems that support the entire application. This experience also taught me the importance of optimising the database and ensuring the backend is scalable and easy to maintain.

Developer Learnings:

Stephen Asiedu

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As a back-end developer, I've come to understand the importance of being familiar with various database systems and modules. This knowledge enables me to build diverse applications and maintain versatility in my work. I've also learned that the responsibility for making the right choices rests on my shoulders, guided by my best judgement.

Developer Learnings:

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Developer Learnings:

Maurquise Williams

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  1. Process of Creating an MVP: Developing a Minimum Viable Product (MVP) taught me how to focus on delivering core functionalities balancing between essential features and avoiding scope creep.
  2. Collaboration in a Real-World Tech Setting: This experience taught me how to collaborate efficiently in a fast-paced tech environment, keeping the team aligned and productive, even while working remotely across time zones.
  3. Sharpening Critical Thinking and Problem-Solving Skills: This experience honed my ability to think critically and solve problems efficiently. By tackling challenges and finding quick solutions, I sharpened my decision-making and troubleshooting skills in a dynamic, real-world setting.

Developer Learnings:

Jeremiah Williams

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All in all this experience was very awesome I learned that in coding with others being transparent is key

Developers Learnings:

Justin Farley

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I learned how important communication is when working with a team. Communication provides understanding, advice, ideas, and much more. While working with the product team, I’ve found that communication keeps everything flowing smoothly. Working with a team also showed me that every member brings something different to the table and we all have to work together in order to align and meet our end goal.

Full Team Learning