There are different ways to assess and track mood, each with its own strengths and limitations. Some methods rely on simple numerical ratings, while others categorize emotions into predefined labels or use more complex models to capture emotional nuances. Choosing the right approach can significantly impact how well you understand your emotions and recognize patterns over time.
In our previous blog post , we explored why mood tracking is important and how it can help with self-awareness and emotional regulation. In this post, we’ll compare three common mood assessment methods used in modern mood trackers, analyzing their pros and cons to help you decide which one best fits your needs.
1. Self-Reporting Scales (Likert Scales, Numeric Ratings, and Sliders)
How It Works
Self-reporting scales are one of the most commonly used methods for mood tracking. Users are asked to rate their mood on a numerical scale, such as 1–5, 1–10, or by using a slider ranging from “Very Bad” to “Very Good.” Some apps also allow users to add brief notes alongside their ratings to provide context.
Pros
✔ Simple and quick to use – Logging a mood takes only a few seconds, making it easy to track emotions consistently.
✔ Widely adopted – Many apps use this method, making it familiar and accessible.
Cons
✖ Oversimplifies emotions – A single number cannot fully capture the complexity of emotions.
✖ Does not differentiate between emotions with the same valence – On a graph, sadness and anger both appear as negative moods, just as joy and contentment both appear as positive, even though they are distinct emotions.
2. Emotion Category Selection (Basic Emotion Labels or Custom Tags)
How It Works
Instead of assigning a numerical value to mood, this method allows users to select from a set of predefined emotion categories, such as Happy, Sad, Angry, Anxious, or Calm. Some apps also let users create custom emotion labels to better match their personal experience.
Pros
✔ More descriptive than numeric ratings – Helps users express emotions more accurately than just choosing a number.
✔ Allows differentiation between emotions with the same valence – Unlike self-reporting scales, this method can distinguish sadness from anger and joy from peace.
Cons
✖ Lacks depth and intensity measurement – Selecting an emotion does not indicate how strong or weak the feeling is.
✖ Doesn’t allow visualization of mood trends on a timeline – Since emotions are stored as categories rather than numerical values, it is harder to track patterns and fluctuations over time.
3. Two-Dimensional Model (Valence-Arousal Approach)
How It Works
The Two-Dimensional Model of Emotions represents mood along two axes:
- Valence – Ranges from negative (unpleasant) to positive (pleasant) emotions.
- Arousal – Ranges from low-energy (calm emotions like relaxation or sadness) to high-energy (intense emotions like excitement or anger).
By placing moods on this coordinate system, users can track their emotional states more precisely. For example, both anger and sadness are negative emotions, but anger has high arousal while sadness has low arousal. Similarly, both excitement and contentment are positive, but excitement is high-energy while contentment is calm.
Pros
✔ Captures a broader spectrum of emotional states – Unlike simple mood ratings, this model accounts for both emotional intensity and positivity/negativity.
✔ Well-suited for visualizing mood patterns over time – By mapping emotions as coordinate points, this method creates a clear, structured representation of mood trends, making it easier to track emotional shifts on a timeline.
Cons
✖ Requires an initial learning curve – Users unfamiliar with the model may need time to understand how to categorize their emotions within the valence-arousal framework.
Conclusion
Different mood assessment methods offer unique advantages and limitations, making them suitable for different tracking needs. Self-reporting scales provide a quick and simple way to log emotions but lack depth. Emotion category selection allows for more structured labeling of moods but makes it difficult to analyze trends over time. The Two-Dimensional Model of Emotions offers a more detailed perspective by mapping emotions along valence and arousal, making it especially useful for tracking emotional trends visually.
So, which approach does Moodset choose? In fact, we combine two methods—the Two-Dimensional Model of Emotions and Emotion Category Selection. This allows users to both name their moods and place them on a valence-arousal scale. However, our main focus is on the Two-Dimensional Model, as it provides deeper insights into emotional patterns and trends over time.