## 4 Discussion  
The efforts throughout this research have been focused on providing robust and well-founded answers to the key questions that drove this study. RQ1 asks what are the prevailing trends in research on \"affective computing\", \"sentiment analysis\", and \"emotion recognition\" from 2018 to 2023, in terms of unimodal, multi-physical, multi-physiological, and multi-physical-physiological approaches. Addressing this RQ, from the detailed examination of studies classified under the different modalities, several prominent trends have emerged. Among the unimodal approaches analyzed in this systematic review, facial expression recognition, text analysis, and speech recognition have garnered significant attention based on the volume of published studies. In terms of physical signals, these modalities have emerged as prominent areas of research. Additionally, the analysis of EEG signals has notably stood out among physiological signal analyses. Considering these findings, the prevailing trends in applications within these domains are as follows:  
* Facial expression analysis approaches are currently being applied across various domains, including naturalistic settings (\"in the wild\"), on-road driver monitoring, virtual reality environments, smart homes, IoT and edge devices, and assistive robots. There is also a focus on mental health assessment, including autism, depression, and schizophrenia, and distinguishing between genuine and unfelt facial expressions of emotion. Efforts are being made to improve performance in processing faces acquired at a distance despite the challenges posed by low-quality images. Furthermore, there is an emerging interest in utilizing facial expression analysis in human-computer interaction (HCI), learning environments, and multicultural contexts. * The recognition of emotions through speech and text has experienced tremendous growth, largely due to the abundance of information facilitated by advancements in technology and social media. This has enabled individuals to express their opinions and sentiments through various media, including podcast recordings, live videos, and readily available data sources such as social media platforms like Twitter, Facebook, Instagram, and blogs. Additionally, researchers have utilized unconventional sources  
Figure 7: Quantity of articles classified as multi-physical–physiological published from 2018 to 2023.  
like stock market data and tourism-related reviews. The variety and richness of these data sources indicate a wide range of segments where such emotion recognition analyses can be applied effectively. * EEG signals continue to be a prominent modality for emotion recognition due to their highly accurate insights into emotional states. Between 2022 and 2023, studies in this field experienced exponential growth. The identified trends include utilizing EEG for enhancing human-computer interaction, recognizing emotions in various contexts such as patients with consciousness disorders, movie viewing, virtual environments, and driving scenarios. EEG is being used for detecting and monitoring mental health issues. There is also a growing focus on personalization, leading towards more individualized and user-specific emotion recognition systems, Other physiological signals, such as ECG, EDA, and HR, are also gaining attention, albeit at a slower pace. * In the realm of multi-physical, multi-physiological, and multi-physical-physiological approaches, it is the former that appears to be laying the groundwork, as evidenced by the abundance of studies in this area. The latter two approaches, incorporating fusions with physiological signals, are still relatively scarce but seem to be paving the way for future researchers to contribute to their growth. Multimodal approaches, which integrate both physical and physiological signals, are finding diverse applications in emotion recognition.