Teen Journal Emotion Tracking

ISEF Category: Behavioral and Social Sciences

Subcategory: Development  ·  Difficulty: Advanced  ·  Setup: University Lab  ·  Time: Full Year

The Hook

Your journal can change before you notice. As you move through high school, the words you use for feelings can get sharper, not just happier or sadder. That shift is called emotional granularity, and it tells you how precisely you name what you feel. With consent and careful privacy rules, you can test whether those word patterns change from grade 9 to grade 12.

What Is It?

Emotional granularity means how many different emotion words you use, and how well you tell those feelings apart. If one student writes only sad, mad, and happy, that is a small box of crayons. If another student uses words like worried, disappointed, relieved, frustrated, and calm, that student is coloring with a much wider set.

This project uses text mining, which means using code or a clear tagging system to count patterns in writing. You are not judging whether a journal entry is good or bad. You are measuring language. Over time, you can see whether students use more distinct emotion words, whether they separate positive and negative feelings more clearly, and whether that changes as school demands change.

Why This Is a Good Topic

This is a strong science fair topic because you can measure a real change over time, compare groups with clear rules, and connect your work to adolescent development and mental health communication. You do not need a wet lab, but you do need careful planning, consent, and a clean way to code text. You can learn how to organize data, define a variable, control for entry length, and test whether language changes across grade levels.

Research Questions

• How does emotional granularity in journal entries change from grade 9 to grade 12?
• What is the effect of school year stress points on the number of distinct emotion words used?
• Does the length of a journal entry change the number of unique emotion words you see?
• To what extent do weekday and weekend entries differ in emotional granularity?
• Which emotion categories, like joy, anger, fear, or sadness, show the biggest change across grades?
• What is the effect of prompt type in the app on the variety of emotion words used?

Basic Materials

• Consent and assent forms for each participant.
• Journaling app export files or text exports from a consented sample.
• Password-protected laptop or desktop computer.
• Spreadsheet software such as Google Sheets or Excel.
• Plain text editor for cleaning entries.
• Secure folder or encrypted drive for storing de-identified data.
• A simple emotion-word codebook that you create before analysis.

Advanced Materials

• Anonymized journal exports with date stamps and grade labels.
• Secure encrypted storage for human-subjects data.
• Python environment for text cleaning and analysis.
• Jupyter Notebook for documenting code and results.
• Annotation software for manual coding checks.
• Statistical software for mixed-effects models or repeated-measures tests.
• A vetted emotion lexicon or custom dictionary for emotion-word tagging.
• Human-subjects approval materials and data-management plan.

Software & Tools

• Python: Cleans text, counts emotion words, and runs time-based comparisons.
• Jupyter Notebook: Keeps your analysis steps readable and easy to rerun.
• pandas: Organizes entries, dates, grades, and coded variables.
• Google Colab: Gives you a free place to run Python if your computer is slow.
• RStudio Desktop: Helps you run statistics and make clear graphs.

Experiment Steps

1. Define the consented dataset you will analyze and decide how you will hide identities.
2. Choose one main measure of emotional granularity, such as distinct emotion words per entry or per 100 words.
3. Build a codebook that groups emotion words into a small set of clear categories before you look at results.
4. Split the data into time slices, such as grade, semester, or month, so you can track change within the same students.
5. Plan controls for entry length, prompt type, and day of week so you do not mix writing style with emotional change.
6. Pre-plan the graphs and statistical tests you will use before you inspect outcomes.

Common Pitfalls

• Mixing students who gave consent with students who did not, which can break your dataset and violate privacy.
• Counting every mood word as a new emotion category, which inflates emotional granularity without a clear codebook.
• Comparing raw word counts across long and short entries, which makes chatty writers look more emotionally complex.
• Letting app prompts change from one term to the next, which can shift the language you measure more than the student’s development.
• Searching only for obvious words like happy or sad, which misses richer terms like frustrated, relieved, or anxious.

What Makes This Competitive

A competitive version of this project uses a clean longitudinal design, strong privacy handling, and a measure that is more precise than simple sentiment counts. You can push it further by comparing multiple emotion dictionaries, checking human coding against automated coding, and controlling for entry length and prompt type. Strong entries also test whether change happens evenly across students or only in certain subgroups. That kind of analysis shows real skill, not just data collection.

Project Variations

• Compare emotion-word growth in journal apps versus handwritten journals from the same grade band.
• Track emotional granularity before and after major school milestones, such as exams, auditions, or sports season.
• Compare neutral prompts with reflection prompts to see which one leads to richer emotion language.

Learn More

• PubMed: Search review articles on emotional granularity, adolescent development, and emotion regulation.
• PubMed Central: Find free full-text papers on teen emotion language and text analysis.
• NIH National Institute of Mental Health: Read background on adolescent mental health and emotional development.
• OpenStax Psychology 2e: Review free chapters on emotion, memory, and development.
• APA Dictionary of Psychology: Look up terms like emotional granularity, affect, and self-regulation.