# Connecting the Elderly Using VR: A Novel Art-Driven Methodology
## 3 Materials and Methods
### Evaluation Approach
#### 3.3.2 Image Sentiment Analysis and Behavioral Analysis  
Leveraging image sentiment analysis modules can enable the development of multimodal systems capable of assessing human emotional responses within 3D spaces, influencing architectural design principles. The inherent subjectivity governing human emotions introduces risks in sentiment extraction tasks [44]. The utilization of Convolutional Neural Networks (CNNs) proves to be an effective method for extracting a viewer's emotional stimuli [45; 46; 47; 48]. The literature has presented various approaches to sentiment expression, categorized primarily into two models for emotion modeling: the Dimensional Model and the Categorical Model [45]. The Dimensional Model conceptualizes emotions as points in a 2D or 3D space, defined by three basic dimensions--valence, arousal, and control. Given the minimal impact of the control dimension (dominance), the existing literature primarily focuses on valence and arousal (Figure 2). Some studies solely consider the valence dimension, predicting sentiment polarity in terms of two levels (positive, negative), three levels (positive, neutral, negative), or more, as demonstrated in [49], where a five-level polarity approach is adopted. Contrastingly, the Categorical Model identifies specific emotions such as \"anger\" or \"fear\", which can also be mapped within the valence-arousal-control space [50]. Numerous \begin{table}
\begin{tabular}{l l l} \hline \hline \multicolumn{2}{l}{How intense was your feeling while:} & \multicolumn{1}{l}{High} \\ - & Navigating in the village & Neutral \\ - & Watching the windows & Low \\ - & Interacting with the village elements(landmarks) & \\ - & Interacting with others & \\ - & Navigating in the apartment (interior design) & \\ \hline \multicolumn{2}{l}{Did the interaction with village triggered your memories?} & Yes \\ & & No \\ & & I don’t know \\ \hline \multicolumn{2}{l}{If yes, what kind of memories?} & \multicolumn{1}{l}{Positive life experience} \\ \multicolumn{2}{l}{Negative life experience} \\ \hline \multicolumn{2}{l}{To what extent do you agree to the following:} & \multicolumn{1}{l}{Strongly disagree (1)} \\ - & The concept is original & Disagree (2) \\ - & The concept is useful to fight social isolation & Neither agree nor disagree (3) \\ - & The concept is useful to stimulate creativity & Agree (4) \\ - & The concept is useful to stimulate creativity & Strongly agree (5) \\ \hline \multicolumn{2}{l}{Would you like to invite your family and friends to the village?} & Yes \\ & & No \\ & & I don’t know \\ \hline \multicolumn{2}{l}{How would you rate your overall village experience?} & Not at all satisfied (1) \\ & & Slightly satisfied (2) \\ & & Moderately satisfied (3) \\ & & Very satisfied (4) \\ & & Completely satisfied (5) \\ \hline \multicolumn{2}{l}{What are your suggestions for improvement of the MindSpaces “virtual village”?} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._studies explore emotion categories, incorporating frameworks like [PERSON]'s Wheel of Emotions [51] and [PERSON]'s theory [52]. In this study, we embrace the Dimensional Model, considering both valence and arousal dimensions. We employ a three-level sentiment polarity, with three classes for valence (positive, neutral, negative) and arousal (excited, neutral, calm), as illustrated in Figure 2. The methodology proposed in [53] is adopted to assess user sentiment while navigating the VR platform. For this study, a behavioral data analysis module for stress detection was also implemented. The method is an adaptation of the method presented and validated in [56], where the authors exploit motion trajectory data for stress detection of office workers. The analysis of behavioral data involves a two-step process: feature extraction and the application of a Hidden Markov Model (HMM) to derive stress levels from user positional data. Four distinct features are extracted from the positional data, namely moving time, track spread, wandering style, and hotspot.