The following three studies serve as examples of articles containing the term \"emotion recognition\" in their titles: \"Spatio-Temporal Encoder-Decoder Fully Convolutional Network for Video-Based Dimensional Emotion Recognition\" [6], \"Applying Self-Supervised Representation Learning for Emotion Recognition Using Physiological Signals\" [7], and \"A Multitask Learning Model For Multimodal Sarcasm, Sentiment And Emotion Recognition In Conversations\" [8]. Despite all including the concept of emotion recognition in their titles, their approaches are markedly distinct. This underscores the importance of analyzing and classifying studies in this systematic review based on the type of data used in their analysis, rather than solely on the keywords of the research field to which they belong. When analyzing the types of data used, these studies can be categorized as follows: the first one falls into the \"unimodal\" category, given its focus only on facial emotion analysis; the second could be classified as \"multi-physiological\", as it integrates various physiological signals for emotion recognition; and the third can be categorized as \"multi-physical\", as it examines visual, acoustic, and textual physical signals derived from dialogue videos. In a similar fashion, the studies examined in this review were categorized based on the type of data they investigate. Affective computing is a broader field that encompasses emotion recognition but also considers the broader spectrum of human affective states, which include emotions, moods, and attitudes. It takes into account not only human expressions but also the interpretation of those expressions by machines. Its objective is to enable computers to recognize and respond to human emotional cues to enhance user experiences. This includes recognizing user emotions, adapting system behavior accordingly, and even generating emotional responses [9; 10]. Similar to the previously mentioned examples, the following studies feature the term \"affective computing\" in their titles: \"Deep learning for affective computing: Text-based emotion recognition in decision support\" [11], \"Asian Affective and Emotional State (A2 ES) Dataset of ECG and PPG for Affective Computing Research\" [12], and \"Utilizing Deep Learning Towards Multi-Modal Bio-Sensing and Vision-Based Affective Computing\" [13]. However, when examining the types of data employed, these studies can be categorized as follows: the first one falls within the \"unimodal\" category, as it primarily focuses only on textual data. The second can be classified as \"multi-physiological\", given its integration of ECG and PPG signals for affective computing research. Lastly, the third can be categorized as \"multi-physical-physiological\" since it examines both facial signals, which are physical data, and various physiological signals, for affective computing research. Sentiment analysis, also known as opinion mining, is specifically focused on determining the sentiment or opinion expressed in text or speech. It is often applied to social media, product reviews, and customer feedback. Its primary goal is to classify text as positive, negative, or neutral, and sometimes to identify more specific sentiments, like joy, anger, or sadness. The focus is on understanding public opinion and feedback. Basedon the types of data, the following two studies can be categorized as \"multi-physical\": \"An Ensemble-Learning-Based Technique for Bimodal Sentiment Analysis\" [14] and \"Tree-Based Mix-Order Polynomial Fusion Network for Multimodal Sentiment Analysis\" [15], since the first one examines audio and text, while the second one analyzes audio, text, and visual physical data. On the other hand, the study titled \"An Efficient Deep Learning for Thai Sentiment Analysis\" [16] falls into the \"unimodal\" category as it solely employs textual data for its analysis.