Personality is a combination of various characteristics and qualities of an individual. It may be affected by the growth and evolution of one’s values, attributes, relationships with the community, personal memories of life events, habits, and skills. Behaviours and decisions of an individual are largely directed by his/her personality. Identification of such a personality trait can be performed based on an individual’s handwriting features. Handwriting may be unique for each person and a person’s nature, behaviour, and certain psychological aspects can be inferred based on it. It is introduced as the field of graphology, also called graphoanalysis, to analyze personality based on handwriting. We perceived that many researchers have worked on personality and/or behaviour identification based on handwriting, however, most of them were limited to a few numbers of features. According to graphology, there is a vast range of features of handwriting strokes which carry psychological characteristics of the writer. In this survey, we present links between handwriting and personality psychology and examine different mechanisms for features extraction to predict a writer’s personality. Psychologically supported handwriting features help to understand personality traits. The paper relates these features and encourages the use of computer-based graphology for personality prediction. It also discusses applications of graphology in various fields.

Survey on handwriting-based personality trait identification

In today’s digital era, the use of online social media networks, such as Google, YouTube, Facebook, and Twitter, permits people to generate a massive amount of textual content. The textual content that is produced by people reveals essential information regarding their personality, with psychopathy being among these distinct personality types. This work was aimed at classifying input texts according to the traits of psychopaths and non-psychopaths. Several studies based on traditional techniques, such as the SRPIII technique, using small-sized datasets have been conducted for the detection of psychopathic behavior. However, the purpose of the current study was to build an effective computational model for the detection of psychopaths in the domain of text analytics and computational intelligence. This study was aimed at developing a technique based on a convolutional neural network + long short-term memory (CNN-LSTM) model by using a deep learning approach to detect psychopaths. A convolutional neural network was used to extract local information from a text, while the long short-term memory was used to extract the contextual dependencies of the text. By combining the advantages of convolutional neural network and long short-term memory, the proposed hybrid CNN-LSTM was able to yield a good classification accuracy of 91.67%. Additionally, a large-sized benchmark dataset was acquired for the effective classification of the given input text into psychopath vs. non-psychopath classes, thereby enabling persons with such personality traits to be identified.

A Hybrid CNN-LSTM Model for Psychopathic Class Detection from Tweeter Users

Nowadays, there is a digital era, where social media sites like Facebook, Google, Twitter, and YouTube are used by the majority of people, generating a lot of textual content. The user-generated textual content discloses important information about people’s personalities, identifying a special type of people known as psychopaths. The aim of this work is to classify the input text into psychopath and nonpsychopath traits. Most of the existing work on psychopath’s detection has been performed in the psychology domain using traditional approaches, like SRPIII technique with limited dataset size. Therefore, it motivates us to build an advanced computational model for psychopath’s detection in the text analytics domain. In this work, we investigate an advanced deep learning technique, namely, attention-based BILSTM for psychopath’s detection with an increased dataset size for efficient classification of the input text into psychopath vs. nonpsychopath classes.

/pdf/detection-and-classification-of-psychopathic-personality-3c61thryr2.pdf

Detection and Classification of Psychopathic Personality Trait from Social Media Text Using Deep Learning Model

Many evidence suggested that one’s personality can be seen from his/her handwritten scratches, using Graphology analysis. Graphology consists of two techniques, structural and symbol analysis. The structural analysis which sees handwriting structurally as a unit. While symbol analysis based on how to write each letter. With a variety of handwriting features, make identifying personalities with Graphology is not easy to do. Computer-based Graphology is done by examining the handwriting image and then giving suggested personality results based on the pattern of each feature. This research conducted using both techniques of six features. The multi-structure analysis was done to feature of margin, a space between lines, a space between words, slope, and dominant zone, while four specific letters (a’, ‘g’, ‘s’, ‘t’) were analyzed using the Convolutional Neural Networks (CNN) classification approach. The results showed that the accuracy of the structured approach was up to 82.5-100%, while the accuracy of the symbol approach using CNN had an accuracy of up to 98.03% of new data with 7-10 minutes in the training process.

Personality Features Identification from Handwriting Using Convolutional Neural Networks

In the recent times, the social networking sites act as a rich source of information, which is shared among online users, who post comments and express their opinions in the form of likes and dislikes. Such content reflects important clues about the personality and behavior of the online community. The dark triad personality traits, such as the psychopathic behavior of individuals, can be detected using computational models. The earlier studies on the dark triad (psychopath) prediction exploit traditional machine learning techniques with limited dataset size. Therefore, it is required to develop an advanced deep neural network-based technique. In this work, we implement a deep neural network model, namely BILSTM for the efficient prediction of dark triad (psychopath) personality traits regarding online users. Experimental results depict that the proposed model attained an improved AUC (0.82) when compared to the baseline study.

Applying Deep Neural Networks for Predicting Dark Triad Personality Trait of Online Users

Graphology is one of the psychology disciplines which aims to study the personality traits of individuals through interpretation of handwriting. We can get information of oneâ€™s personality through graphology. In addition, by using android based mobile device, graphology analysis could show oneâ€™s personality faster. This study was conducted by taking 42 samples of handwriting from different backgrounds. The feature used in this study was handwriting margin. Besides, Support Vector Machine method was employed to classify the result feature from extraction process. The result of this study showed the accurate average of the application reached 82.738%.

http://jitecs.ub.ac.id/index.php/jitecs/article/download/37/26

Personality Analysis through Handwriting Detection Using Android Based Mobile Device

Grafologi merupakan salah satu cabang ilmu psikologi yang khusus mempelajari tentang tulisan tangan. Melalui grafologi bisa diperoleh informasi tentang karakter kepribadian seseorang. Melalui perangkat mobile berbasis android, analisis grafologi akan menjadi lebih cepat dalam menampilkan pendekatan karakter kepribadian seseorang. Penelitian dilakukan dengan mengambil 42 sampel tulisan tangan dari orang yang memiliki perbedaan latar belakang. Fitur yang digunakan dalam penelitian ini adalah margin kiri pada tulisan tangan. Metode Support Vector Machine digunakan untuk mengklasifikasikan fitur hasil dari proses ekstraksi. Menggunakan metode baru yaitu Grid-double block dengan satu kali proses menghasilkan rata – rata akurasi margin kiri sebesar 69%.

/pdf/metode-grid-double-block-untuk-deteksi-margin-kiri-tulisan-2uy56m8yor.pdf

waskitha wijaya

Papers

Personality Analysis through Handwriting Detection Using Android Based Mobile Device

Metode Grid-Double Block Untuk Deteksi Margin Kiri Tulisan Tangan Pada Aplikasi Grafologi