MOOD METER
PROJECT OVERVIEW | UX RESEARCH | UX DESIGN | UI DESIGN | USABILITY TEST | KEY TAKEAWAYS
Sector- Entertainment, Social Media, Digital Content Creation
UX Techniques Used- User Research, Competitive Analysis, User Personas, Affinity map, Wireframing, Prototyping, Usability Testing
My Role-UI/UX Design
Project Time- 6 Weeks
PROJECT OVERVIEW
Background
YouTube Meter is a project I worked on to design an AI-powered sentiment analysis tool that benefits both YouTube creators and users. The goal was to provide creators with valuable insights into audience sentiment, while also empowering users to make informed decisions about the videos they watch. I worked on this project solo, and it was completed over a period of weeks.
It has become a vital platform for content creators to share their ideas, showcase their talents, and build communities. However, understanding audience sentiment and feedback can be challenging for creators, while users often struggle to gauge the tone and content of videos before watching.
Problem Statement
Through my research, I discovered that creators lack insights into audience sentiment, making it difficult to refine their content strategy and engage with their audience effectively. Users, on the other hand, often watch videos without fully understanding their tone or content, leading to potential misinterpretation or dissatisfaction.
Research Goal
With a clear understanding of the problem, I set out to design an AI-powered sentiment analysis tool, YouTube Meter, that provides creators with valuable insights into audience sentiment and empowers users to make informed decisions about the videos they watch.
UX RESEARCH
Project Goals
After analyzing user insights and research findings, I structured the project goals using a Venn diagram, balancing business objectives, user needs, common goals, and technical considerations to ensure YouTube Meter delivers meaningful value to both creators and viewers.
Methodology
To design an effective sentiment analysis tool for YouTube creators and users, I conducted user research to gather insights into their needs, pain points, and behaviors.
Competitive Analysis: Distributed online surveys to 20 creators and 20 users to collect quantitative data on their habits, preferences, and pain points.
Interviews: Conducted in-depth interviews with 5 YouTube creators and 5 users to gather qualitative data on their experiences, challenges, and needs.
Competitive Analysis
To understand YouTube Meter’s position in the market, I conducted a competitive analysis, performing a SWOT analysis on platforms like TikTok, Vimeo, and Twitch. This helped me identify their strengths, weaknesses, opportunities, and threats, allowing me to refine my approach and ensure YouTube Meter offers unique value to creators and users.
| Category | TikTok | Vimeo | Twitch |
|---|---|---|---|
| Website | tiktok.com | vimeo.com | twitch.tv |
| Mission Statement | "To inspire creativity and bring joy through short-form mobile video." | "To empower creators with professional-quality tools and ad-free viewing." | "To bring people together live every day to create and enjoy entertainment." |
| Target Market | Gen Z, creators, brands, influencers | Filmmakers, businesses, professionals | Gamers, streamers, creative content lovers |
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User Interview
To gain deeper insights, I conducted in-depth interviews with five YouTube creators and five users, gathering qualitative data on their experiences, challenges, and needs. These conversations helped me understand the real struggles of both creators and viewers, shaping the foundation of YouTube Meter. I then organized these insights into an affinity map, identifying key themes and patterns to prioritize features that address user pain points effectively.
User Persona
Building on my research, I developed user personas to represent both YouTube creators and viewers, capturing their goals, frustrations, and behaviors. These personas helped me design YouTube Meter with a user-centered approach, ensuring the tool effectively addresses their unique needs.
Pain Points
Building on my research and personas, I identified key pain points faced by both YouTube creators and viewers. These insights helped me pinpoint challenges that YouTube Meter needed to address, ensuring the tool provides meaningful solutions for sentiment analysis and content evaluation.
Creators: Difficulty understanding audience sentiment, lack of feedback, and challenges in refining content strategy.
Users: Uncertainty about video tone and content, potential misinterpretation, and dissatisfaction.
User Flow
With a clear understanding of user pain points, I designed user flows to map out interactions within YouTube Meter. This helped visualize the journey for both creators and viewers, ensuring a seamless and intuitive experience that enhances sentiment analysis and content evaluation.
Exploration: Visual & Functional Discovery
Designing YouTube Meter came with a unique challenge; it needed to blend naturally with YouTube’s existing look while adding a new feature: sentiment analysis. Since YouTube already has a strong visual identity, I explored ways to build on that style while introducing emotional feedback tools. After mapping out the user flow, I created sketches and a moodboard to help shape the visual direction and layout ideas for this new feature.
Early Sketches
These rough concepts helped me visualize key elements such as:
Where to place sentiment overlays within the video player
How creators could access emotional feedback in dashboards
Different layouts for viewer sentiment summaries
Moodboard
To inspire the visual direction, I curated a moodboard focused on:
YouTube’s native color palette for consistency
Emotion-driven UI cues like heatmaps, gradients, and iconography
Wireframes
With the moodboard and initial sketches in place, I had a clear visual and functional direction for YouTube Meter. I then translated these ideas into structured wireframes—laying out how each feature would appear on screen and ensuring the user journey felt intuitive from start to finish. These wireframes acted as the foundation for testing layout, interaction patterns, and emotional cues within the interface.
UX DESIGN
Design Solutions
With a deep understanding of user pain points and needs, I crafted design solutions that address key challenges faced by both creators and viewers. These solutions ensure YouTube Meter enhances sentiment analysis, refines content strategies, and provides users with clearer insights before engaging with videos.
YouTube Meter is designed to address these pain points by providing:
Real-time Sentiment Analysis: AI-powered analysis of video comments and feedback to provide creators with insights into audience sentiment.
Sentiment Indicator: A visual indicator that displays the overall sentiment of the video, helping users make informed decisions about watching.
Bot Detection: Advanced bot detection capabilities to help creators identify and filter out fake or spam comments, ensuring more accurate sentiment analysis.
Creator Dashboard: A dedicated dashboard for creators to access sentiment analysis results, track audience feedback, and refine their content strategy.
User-Friendly Interface: An intuitive interface that allows users to easily understand the sentiment of videos and make informed decisions.
UI DESIGN
UI Library & Components
To bring YouTube Meter to life visually, I designed UI Library & Components, including typography, a cohesive color palette, and other essential elements. These choices ensured a polished, intuitive interface that enhances readability and user engagement.
High Fidelity Design
With the UI components in place, I brought YouTube Meter to life through high-fidelity designs, refining the visuals, interactions, and overall aesthetics to create a polished, intuitive experience for both creators and viewers.
USABILITY TEST
Iterations
After designing the initial high-fidelity screens for YouTube Meter, I reviewed user feedback and made thoughtful iterations to improve usability. The updated designs reflect clearer visual hierarchy, more intuitive navigation, and refined layout choices. Below is a comparison highlighting the evolution from the original screens to the iterated versions.
To validate MoodMeter’s effectiveness, I developed a usability test plan, evaluating how well the tool helps users make informed decisions through sentiment analysis. I tested two key flows—the Viewer Decision Flow, where users assess the comment sentiment before watching a video, and the Creator Dashboard Flow, where video creators analyze audience feedback and bot detection to refine their content strategy.
Objectives
The goal of this usability test is to evaluate whether MoodMeter effectively helps users make informed decisions based on comment sentiment analysis.
Task Flows Being Tested
Viewer Decision Flow: Users go to the comment section, see the MoodMeter sentiment summary, and decide whether to watch the video or not.
Creator Dashboard Flow: The video creator clicks on MoodMeter, enters the sentiment dashboard, reviews the audience’s response (positive, negative, neutral), sees bot detection on comments, and decides whether to keep or delete the video.
Results
Overall Experience: Participants found YouTube Meter easy to use and valuable for understanding audience sentiment.
Sentiment Analysis: Participants appreciated the real-time sentiment analysis and found it helpful for refining their content strategy.
Bot Detection: Participants found the bot detection feature useful for identifying fake or spam comments.
User Interface: Participants praised the intuitive interface and ease of navigation.
User POV
Creator POV
| Success Metrics | |||
| Metric | Definition | Measurement Method | Target Goal |
| Task Completion Rate | Users successfully navigate MoodMeter without confusion. | Observing if users complete key tasks without errors. | 90%+ users complete tasks smoothly. |
| Decision Accuracy | Users confidently decide to watch or delete based on MoodMeter insights. | Asking users to rate their confidence in MoodMeter decisions after testing. | 80%+ users feel confident in their decisions. |
| Time on Task | How long users take to interpret sentiment and make a choice. | Measuring time spent from sentiment viewing to user action (watching/deleting). | Users complete tasks in under 10 seconds. |
| Error Rate | Number of times users struggle with MoodMeter features. | Tracking confusion points or misclicks. | Less than 5% of interactions result in errors. |
| User Satisfaction Score | Users find MoodMeter easy and helpful. | Post-test survey (1-5 rating scale). | Average satisfaction score of 4+ out of 5. |
KEY TAKEAWAYS
Relevance: YouTube is a vast platform with millions of users and creators, making sentiment analysis a valuable tool.
Impact: A sentiment analysis tool can help creators refine their content strategy and engage with their audience more effectively.
Complexity: The project involves working with AI-powered sentiment analysis, bot detection, and user interface design, making it a challenging and rewarding project.
Real-world application: YouTube Meter has the potential to be a real-world solution for creators and users, making it a meaningful project.
Reflection
Working on YouTube Meter taught me how AI can enhance sentiment analysis and user engagement. My biggest challenge was ensuring the tool felt intuitive while avoiding AI bias and oversimplified judgments. Sentiment analysis can misinterpret sarcasm or cultural nuances, and users sometimes took ratings at face value. If I could improve it, I would refine bias detection, offer context-aware insights, and encourage deeper reflection. This project strengthened my ability to design human-centered AI solutions that go beyond data to create meaningful experiences.