# Federated Learning-Based Analysis of Human Sentiments and Physical Activities in Natural Disasters
## 4 Experimental Results  
### Dataset  
The first step begins with the collection of visuals from social media sites, such as Facebook, Google Images, and Flicker. During crawling for imagery from the aforementioned websites, the copyright element was considered, and only visuals authorized for distribution were selected. In addition, visuals were retrieved for tags, such as storms, seismic events, hurricanes, and tidal waves. These tags were made specific for places, such as \"earthquake in Nepal,\" \"monsoons in Brazil,\" and \"natural disasters in Japan.\"  
The identification of labels for emotions and related behaviors is among the most crucial concerns of this research. The majority of the current works focus on distinct emotions, such as \"negative,\" \"positive,\" and \"neutral,\" with no human-related behaviors [39]. Nevertheless, we want to focus on emotions that are highly pertinent to disaster-related details, for instance, labels like \"grief,\" \"excitement,\" and \"rage\" in catastrophic conditions. Moreover, based on recent research in human psychology [40], we argue that persons who are surrounded by a catastrophe seem to be more likely to express two important sets of human emotions. This investigation revealed various human emotions [29]. The first collection of emotions comprises common human phrases, such as \"negative,\" \"positive,\" and \"neutral.\" The second category has the terms \"pleased,\" \"anxiety,\" \"normal,\" and \"worried.\"  
The final collection is the expanded version of human expressions, which contained additional specific emotions, such as \"anguish,\" \"revulsion,\" \"delight,\" \"wonder,\" \"excitement,\" \"sadness,\" \"pain,\" \"weeping,\" \"scar,\" \"antsy,\" and \"relief.\" These emotions are related to the terms \"seated,\" \"stand,\" \"ran,\" \"lying,\" and \"jogging.\" Table 2 provides a thorough classification of potential human emotions and related human actions under catastrophic situations, and Figure 4 illustrates the selection of images extracted from the dataset. The dataset is made publicly available for academic research [41]. Using individual viewpoints and thoughts on disasters with accompanying external perceptions, the crowdsourcing initiative aims to generate an objective for envisaged human sentiment analysis. During the crowdsourcing research phase, we provided preselected images to the study participants for annotation through Fiverrr [42]. Individuals were labeled with 500 images throughout the procedure. We disseminated images with a questionnaire survey, including a disaster-related image, to participants in order for them to label human emotion and associated behavior. In the initial query, participants were asked to evaluate images on a scale from 1 to 10, where 10 represents 'positive' emotion, 5 represents 'neutral' emotion, and 1 represents 'negative'. The purpose of this inquiry was to ascertain the respondents' general impression of the image. The follow-up question is somewhat more precise, such as'sad', 'happy', 'angry', and 'calm,' and can retrieve the exact emotions represented by an image to respondents. In the third question, respondents were asked to rate the imagery on a scale from 1 to 7 and to describe the emotion expressed by each image. In addition, the participants were requested to describe their sentiments about the presented images and explicitly tag images with a specific sentiment tag if that tag was not included in the list of available tags. The fourth question seeks to identify image characteristics that elicit human emotions and behavior at the threshold of the scenario or \begin{table}
\begin{tabular}{c c} \hline \hline
**Set** & **Sentiments** & **Physical Activities** \\ \hline Set 1 & Neutral, negative, and positive \\ \hline Set 2 & Cheerful, sorrowful, calm, and frightened & Run, sit, lay, and walk \\ \hline Set 3 & Rage, contempt, delight, astonishment, excitement, grief, pain, tears, panic, anxiety, and relief \\ \hline \hline \end{tabular}
\end{table}