# Federated Learning-Based Analysis of Human Sentiments and Physical Activities in Natural Disasters
## 2 Related Work
### Human Sentiment and Associated Activity in Natural Disasters  
In sentiment analysis, ML techniques involving text analytics and biometrics are used to comprehensively recognize, isolate, assess, and analyze emotional states. Studies on inferring sentiments from image features are limited [18]. In addition, because this is a novel and intricate endeavor, the public dataset is reduced, making it challenging to establish firm state-of-the-art that can serve as a benchmark. A study proposed by [PERSON] et al. [19] identified low- to high-level features from the background of objects in pixels to reduce the effectiveness gap. The recognition performance was improved owing to the retrieved features. [PERSON] et al. [20] suggested a model that reduces the effectiveness gap between the retrieval of objects with high and low background feature levels. These extracted features enhance the classification accuracy. The research described in [21] extracted features to classify the emotional reactions elicited by an image using art theory and psychological principles. The retrieved features were organized into categories according to their content, texture, and color before being fed into a naive Bayes classifier for classification. However, the retrieved features failed to discern the intricate association between human emotions and the contents of the images, even though the research produced some encouraging findings at one point. Figure 1 shows the taxonomy of human sentiment analysis using ML and deep learning algorithms.  
Recent research has shown the criteria necessary to extract adjective-noun pairs (ANPs), which may be used to infer the emotional mood conveyed by a picture [23]. Examples of ANPs are \"sad face\" and \"happy wife.\" To make a meaningful contribution to the body of existing research, they assembled a dataset that included 3000 ANPs. In addition, they provided several baseline models that were utilized to assess APN-based approaches [24]. These models have been utilized extensively. [PERSON] et al. [25] proposed a method for predicting sentiment avatars from images. This method was a part of their study. They trained SimleyNet [26] using a dataset that had been obtained primarily from social media. This dataset includes four million images and emoji pairings. Several experts have proposed that sentiment classification should also consider data from visual and textual sources. To analyze sentiments, for instance, the authors [27] presented attention-based modality-gated networks, which made use of imagery that went along with the text.  
The most prevalent high level indications for human activity recognition in images are visible organs that can be easily moved, such as hands, legs, and neck, and the features of a background. Most published research on human activity detection is based on supervised learning [28]. In the context of gesture recognition, deep models require substantial amounts of data to be used during training, and as a solution to this issue, the transfer learning strategy has been extensively researched [29].  
Because many satisfactory results have been produced, it is generally accepted that it is highly expensive to annotate all human actions. Human annotators and deep learning experts have made varying endeavors, and the possibility of error remains significant [1; 30; 31]. Nonetheless, providing accurate and pertinent information is imperative for the detection of human physical actions.  
Crowdsourcing has been conducted to ascertain and annotate human behavioral patterns around natural calamities and associated photos on social networks. Owing to the lack of available datasets and the profusion of unlabeled images posted on social media sites, this is a potential approach to evaluate the proposed research. On the one hand, the suggested framework enables us to circumvent problems associated with the lack of training sample availability. On the other hand, it will allow blended learning in a plurality scenario without data sharing, resulting in an increase in data privacy.