AI Emotion Tracker: An Intelligent Mood Prediction System Based on Diary Text

This article introduces the AI Emotion Tracker course project developed by mercury-air24. The project uses natural language processing technology to analyze diary texts, extract emotional signals, and predict mood change trends, providing intelligent tools for mental health monitoring and emotional management. The core of the project lies in converting unstructured diaries into quantifiable emotional indicators, covering text preprocessing, feature extraction, emotion prediction, and trend visualization, while also discussing technical paths, application value, and challenges faced.

Mental health issues are receiving increasing attention in modern life, but traditional emotion tracking relies on subjective ratings or regular consultations, making it difficult to capture dynamic changes. With the advancement of natural language processing technology, AI has shown potential in understanding human emotions, and this project is an innovative attempt to apply AI to personal emotional management.

The system workflow includes: 1. Text input and preprocessing: cleaning, word segmentation, and stopword removal; 2. Emotional feature extraction: extracting multi-dimensional information such as emotional vocabulary frequency, sentence structure, and semantic polarity from texts; 3. Emotional state prediction: classifying/scoring emotions to identify positive, negative, or neutral tendencies; 4. Trend analysis and visualization: displaying time-series graphs of emotional changes to help identify fluctuation patterns and triggering factors.

Key links in the technical architecture include: text vectorization (TF-IDF, Word2Vec, BERT embedding, etc.); emotion analysis models (rule-based methods, traditional machine learning such as SVM, deep learning such as LSTM); time-series modeling (capturing dynamic emotional processes); and personalized adaptation (learning user language styles).

The social value of the project is reflected in: early warning mechanism (identifying abnormal low trends and alerting); self-awareness improvement (helping users understand emotional triggers and coping patterns); auxiliary psychological treatment (providing therapists with patients' daily emotional data); and lowering service thresholds (low cost, always available, and good privacy).

The challenges faced by the technology include: language complexity (difficulty in handling rhetorical devices such as sarcasm and metaphors); privacy and ethics (diary data security, informed consent issues); over-reliance risks (should not replace users' real feelings); and cultural differences (model adaptability to emotional expressions from different cultural backgrounds).

This project is a beneficial exploration of AI in the field of mental health. Although it is a course project with imperfections, its concept of using technology to help people manage emotions is worthy of recognition. In the future, we look forward to more accurate intelligent systems, but humanistic care is always the core of the mental health field, and technology should serve as an auxiliary rather than a replacement. This project provides an introductory reference for the interdisciplinary field of AI + mental health.