[2026 Latest] Multimodal Analysis of NLP and Attendance Time-Series Data: An Approach to Maximizing the Accuracy of Resignation Sign Detection

In 2026, as the decline in the working population accelerates, "talent retention" is one of the top management priorities for companies. Traditional survey-based engagement studies have faced challenges such as response bias and time lags. Currently, in the cutting-edge HR Tech field, "multimodal analysis"—which integrates Natural Language Processing (NLP) data obtained from chat tools like Slack and Teams with time-series data from timecards and attendance records—has become the mainstream. This article provides a detailed explanation of the technical approach to detecting resignation signs with high precision by combining unstructured and structured data.

1. Why Multimodal Analysis is Essential for Resignation Prediction

Traditional resignation prediction models relied solely on "structured data" such as increases in overtime hours or paid leave utilization rates. However, it is difficult to capture "quiet quitting" or sudden drops in motivation using these alone. Multimodal analysis captures changes in individual behavior from multiple perspectives by integrating unstructured text data with numerical attendance data.

According to the latest statistics, companies that have adopted a multimodal approach have seen a significant improvement in prediction accuracy (F1 score) compared to models using a single data source.

As shown in the graph above, accuracy reaches up to 89% by combining both types of data. This is because the signs of "slight increases in tardiness" appearing in timecard data and the "increase in negative vocabulary" on chat platforms complement each other.

2. Extracting Sentiment and Context through Natural Language Processing (NLP)

In analysis utilizing NLP, context analysis using LLMs (Large Language Models) is crucial, going beyond simple keyword extraction. For example, it scores the tone of sentences following standard phrases, decreases in response speed, and the frequency of mentions of specific topics (career, dissatisfaction, fatigue).

In particular, identifying linguistic patterns indicating a lack of "psychological safety" early on is key to preventing turnover. Data analysts track these sentiment scores over time and build systems to issue alerts when a sharp downward trend is observed.

3. Integration with Attendance Time-Series Data: Building an Anomaly Detection Engine

Attendance data is analyzed using RNNs (Recurrent Neural Networks) or LSTMs (Long Short-Term Memory). The focus is not just on "high volume of overtime" but on "changes in rhythm." For instance, if overtime that used to occur every Wednesday suddenly disappears, it may suggest that the employee is securing time for interviews for job-hunting activities.

These time-series features and the sentiment features obtained from NLP are integrated using a neural network equipped with an Attention mechanism. This makes it possible to calculate resignation risk from both "behavioral" and "psychological" aspects. Based on this score, HR managers can take concrete actions, such as conducting 1-on-1 meetings at the appropriate time.

4. Privacy Protection and Ethical Considerations in Implementation

Extremely high ethical standards are required for the analysis of employee data. To avoid infringing on individual privacy, data anonymization and ensuring transparency regarding the purpose of analysis are essential. AI-based prediction is strictly for "support," and a "Human-in-the-loop" design—where the final judgment is made by a human—is vital for maintaining organizational trust.