## 4 Discussion
These range from healthcare systems, individual and group mood research, personality recognition, pain intensity recognition, anxiety detection, work stress detection, stress classification and security monitoring in public spaces, to vehicle security monitoring, movie audience emotion recognition, applications for autism spectrum disorder detection, music interfacing, and virtual environments. RQ2 asks what are the most commonly used data analysis models in studies of \"affective computing\", \"sentiment analysis\", and \"emotion recognition\", and how have they evolved in terms of applications and approaches over the examined period. Addressing this RQ, we find that the articles in this review cover a wide range of topics within these fields. They utilize diverse data sources, including physiological signals, voice, text and facial expressions, to name a few. The diversity of these articles reflects the interdisciplinary nature of their applications in healthcare, social network analysis, human-computer interaction, and other fields. The extensive analysis conducted in this review highlights a variety of prominent data analysis models and approaches, as detailed in the following list:  
* Bidirectional encoder representations from transformers: Used in sentiment analysis and emotion recognition from text, BERT models can understand the context of words in sentences by pre-training on a large text and then fine-tuning for specific tasks like sentiment analysis. * CNNs: These are commonly applied in facial emotion recognition, emotion recognition from physiological signals, and even in speech emotion recognition by analyzing spectrograms. * RNNS and variants (LSTM, GRU): These models are suited for sequential data like speech and text. LSTMs and GRUs are particularly effective in speech emotion recognition and sentiment analysis of time-series data. * Graph convolutional networks (GCNs): Applied in emotion recognition from EEG signals and conversation-based emotion recognition, these can model relational data and capture the complex dependencies in graph-structured data, like brain connectivity patterns or conversational contexts. * Attention mechanisms and transformers: Enhancing the ability of models to focus on relevant parts of the data, attention mechanisms are integral to models like transformers for tasks that require understanding the context, such as sentiment analysis in long documents or emotion recognition in conversations. * Ensemble models: Combining predictions from multiple models to improve accuracy, ensemble methods are used in multimodal emotion recognition, where inputs from different modalities (e.g., audio, text, and video) are integrated to make more accurate predictions. * Autoencoders and generative adversarial networks (GANs): For tasks like data augmentation in emotion recognition from EEG or for generating synthetic data to improve model robustness, these unsupervised learning models can learn compact representations of data or generate new data samples, respectively. * Multimodal fusion models: In applications requiring the integration of multiple data types (e.g., speech, text, and video for emotion recognition), fusion models combine features from different modalities to capture more comprehensive information for prediction tasks. * Transfer learning: Utilizing pre-trained models on large datasets and fine-tuning them for specific affective computing tasks, transfer learning is particularly useful in scenarios with limited labeled data, such as sentiment analysis in niche domains. * Spatio-temporal models: For tasks that involve data with both spatial and temporal dimensions (like video-based emotion recognition or physiological signal analysis), models that capture spatio-temporal dynamics are employed, combining approaches like CNNs for spatial features and RNNs/LSTMs for temporal features.