Personalized Stress Detection Using Lightweight Deep Learning Models

Stress has emerged as a pervasive issue in contemporary society, affecting individuals across various age groups and backgrounds. Given its widespread implications for mental health and overall well-being, developing efficient and personalized stress detection techniques is paramount. Although several stress detection methods exist, many are undermined by high computational complexity and prolonged running times. This article delves into a lightweight deep learning model aimed at stress detection utilizing social media tweets, focusing on pre-processing, feature extraction, and sentiment classification.

The Workflow of Stress Detection

The proposed methodology for stress detection involves a structured workflow, as illustrated in Figure 1. This workflow encompasses five primary stages: data collection, pre-processing, feature extraction, feature fusion, and classification via a model to detect stress.

Pre-Processing Techniques

The pre-processing phase plays a crucial role in enhancing data quality. It employs four main techniques:

1. Tokenization: This breaks text down into smaller components or tokens. For instance, a word like "walking" would be simplified to "walk."

2. Removing Punctuation & Symbols: Unnecessary punctuation (e.g., commas, question marks) and symbols (e.g., @, #) are stripped from the text to leave a cleaner dataset. For instance, "The world is beautiful!" becomes "The world is beautiful."

3. Stemming: This process converts words to their base forms by eliminating suffixes, improving the effectiveness of the data. Thus, "running" might be reduced to "run."

4. Stopword Removal: Words that do not contribute significantly to meaning—like "the," "is," and "at"—are eliminated, streamlining the data without losing essential context.

Feature Extraction Phase

The extracted features are critical for accurate stress classification. The proposed technique utilizes four different methods to generate a unique feature vector for each tweet:

1. FastText: FastText focuses on word representation through character n-gram embeddings, allowing it to effectively manage out-of-vocabulary words. For example, "unhappiness" could benefit from knowing the embeddings for "un" and "happiness."

2. GloVe: The GloVe algorithm captures semantic relationships between words by evaluating how frequently they co-occur in a given dataset, making it a strong candidate for understanding context in tweets.

3. DeepMoji: This method leverages emojis to encapsulate emotional context in the text, particularly useful in social media environments where tweets often incorporate emotional cues through emojis.

4. XLNet: A more advanced technique, XLNet utilizes permutation language modeling to consider all possible word orders within a given input, thus capturing more comprehensive contextual information.

Feature Fusion Using Weighted Mechanisms

After extracting features from the various methods, the next step is feature fusion. Here, a weighted fusion mechanism is applied, assigning importance to each feature based on its relevance. The combined features are mathematically represented as follows:

[
XF = \sum\limits{a = 1}^{n} {u_a \cdot X_a}
]

In this equation, (X_F) signifies the fused feature vector, and (u_a) indicates the weight assigned to each feature from the extraction technique (a).

Stress Detection Through the DSC-ResNet Model

The final phase involves the use of the DSC-ResNet model for stress detection. This model integrates a Depth Separable Convolutional Neural Network (DSCNN) with a Residual Network (ResNet) architecture, optimizing it for efficient computation.

DSCNN enhances performance by separating the spatial filtering and linear combinations of input channels, reducing unnecessary calculations. As a result, it provides a more compact and efficient model while maintaining accuracy.

On the other hand, the ResNet architecture improves the training efficiency of deep networks through its unique residual block design. This structure allows for better backpropagation of gradients, facilitating more effective learning.

Hyperparameter Tuning with the CFFO Algorithm

To maximize the model’s effectiveness, hyperparameter tuning is leveraged through the CFFO algorithm, which mimics the behavior of the fennec fox. This metaheuristic approach operates in two phases:

1. Exploitation Phase: This phase focuses on local searches to converge toward a more optimal solution, akin to a fennec fox digging for prey.

2. Exploration Phase: The exploration phase seeks to avoid local optima, similar to a fennec fox’s escape strategy from predators.

The optimization process involves generating an initial population of candidate solutions (represented by fennec foxes) and iteratively refining them based on fitness evaluations.

The pseudo-code for the CFFO algorithm summarizes the methodology’s structure, allowing for the precise orchestration of stress detection processes.

Conclusion

This detailed exploration of a personalized stress detection method sheds light on an innovative use of lightweight deep learning models. By effectively combining pre-processing techniques, feature extraction methods, and advanced classification frameworks, this approach not only aims to enhance the accuracy of stress detection but also facilitates its execution in real-time applications, such as through social media analysis. The robust workflow ensures that the resultant insights are well-founded, potentially leading to more substantial mental health interventions tailored to individual needs.