# A Multimodal Affective Sensing Model for Constructing a Personality-Based Financial Advisor System
## 2 Related Work
[PERSON] [36] combined the Active Appearance Model (AAM) and Convolutional Neural Network (CNN) algorithms in the FFHQ dataset for a personality classification task on facial features, and the experimental results showed an average accuracy of 87.9%. As an expression of human behavior signals, audio data also have many excellent applications for personality prediction. Common feature extraction methods are the use of low-level descriptors or prosodic features as input signals for automatic personality trait recognition [37; 38]. These methods suggested that audio information has a significant impact on the ability to recognize personality. Some studies used information from social media to identify user personalities, while others used common methods for detecting personalities in texts, such as LIWC [39] to extract various psychologically related words as features to predict the personality of the text authors [40; 41]. There are also document-level detection and classification applications: [PERSON] [42] proposed a 2 CLSTM model (CNN + BiLSTM) and extracted latent sentence groups (LSG) from text as input features, in two different types of datasets (long text and short texts), achieving good results. At the same time, we found that most previous studies focused on analyzing the influence and association of behavioral signals and emotions, so there are numerous open-source databases available to researchers; however, there are relatively few publicly available datasets on personality and human behavioral signals, especially multimedia datasets. For this reason, many researchers created databases for their research. We compiled a list (Table 2) of recent studies on the analysis of personality traits through human behavioral signals. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Dimensions of Personality** & **Personality Traits** \\ \hline The Big Five Model & 5 & Openness; Conscientiousness; Extraversion; \\ Myers–Briggs Type Indicator (MBTI) & 4 & Extraversion/Introversion; Sensing/Intuition; Thinking/Feeling: Judging/Perceiving Warmth; Intellect; Liveliness; Duiffulness; Sensitivity; Paranova; Abstractiveness \\ [PERSON]’s 16 Personality Factor & 16 & Introversion; Anxiety; Emotional stability; Aggressiveness; Openmindedness; Independence; Perfectionism; Tension; Social assertiveness \\ PEN Model & 3 & Psychoticism-Normality; Extraversion-Introversion; Neuroticism-Emotional Stability \\ HEXACO model & 6 & Honesty–Humility; Emotionality; Extraversion; Agreeableness; Conscientiousness; Openness to Experience \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The major personality traits models. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Author** & **Participants** & **Classes of Human Behavior Signal** \\ \hline [PERSON] [7] & 28 & EEG, GSR, PPG \\ [PERSON] [8] & 14 & Lexical, Speech, Visual \\ [PERSON] [10] & 210 & Lexical, Speech, Visual \\ [PERSON] [30] & 120 & Visual \\ [PERSON] [31] & 128 & Lexical, Speech, Visual \\ [PERSON] [35] & 13,347 & Visual \\ [PERSON] [43] & 45 & Speech, Visual \\ [PERSON] [44] & 24 & Speech, Visual \\ [PERSON] [45] & 106 & Lexical, Speech, Visual \\ [PERSON] [46] & 128 & Visual \\ [PERSON] [47] & 60 & Lexical, Speech, Visual \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Recent studies on the analysis of personality traits through human behavioral signals. The relationship between the Big Five personality traits and financial behavior is well documented in behavioral finance research. For investment risk management, [PERSON] [48] believes that risk is an important factor to consider when making investment decisions; she used a multivariate regression analysis model to determine from investors that people with agreeableness, extroversion, and openness to experience are related to risk tolerance. [PERSON] [49] analyzed the effects of the Big Five personality traits and emotions on investor risk aversion, investment intentions, and investment choices. They validated them with statistical methods such as ANOVA and t-tests.