TFG/TFM: Tracking Emotional Trends in Mobile App Features Over Time

Motivation: User reviews provide valuable feedback about how people experience mobile apps. Emotions expressed in these reviews can reveal satisfaction, frustration, or excitement about specific features. Understanding how these emotions change over time can help developers assess the impact of updates, bug fixes, or design changes.

Objectives: The goal of this project is to analyze the temporal evolution of emotions expressed in app reviews at the feature level. Using existing methods for feature extraction and emotion classification, the student will:

• Aggregate and visualize emotion trends across different app versions or time periods.
• Identify features with significant emotional shifts (e.g., from sadness to joy).
• Explore correlations between emotional changes and app release events.

Expected Results:

• A dataset enriched with timestamps, features, and emotion labels.
• Visualizations (graphs or dashboards) showing how user emotions evolve over time for key features.
• A short analysis highlighting which updates improved or worsened user sentiment toward specific features.

Technologies:  Python (Pandas, Matplotlib/Plotly, PyTorch/Tensorflow) for data processing, analysis, and model integration; Angular or React (or another JavaScript framework) for visualization and interactive dashboards; and APIs or web services for data access and integration.

Interested in this project? Contact us!