# Machine Learning Approach for Personality Recognition in Spanish Texts
## 1 Introduction  
Personality has been recognized as a driver of decisions and behavior; it consists of singular characteristics on how individuals think, feel, and behave [1]. Understanding personality provides a way to comprehend how the different traits of an individual merge as a unit, since personality is a mixture of traits and behavior that people have to cope with situations. Personality influences selections and decisions (e.g., movies, music, and books) [2]. Personality guides the interactions among people, relationships, and the conditions around them. Personality has been shown to be related to any form of interaction. In addition, it has been shown to be useful in predicting job satisfaction, success in professional relationships, and even preference for different user interfaces [3]. Previous research on user interfaces and personality has found more receptiveness and confidence in users when the interfaces take personality into account. When personality is predicted from the social media profile of users, applications can use it to personalize presentations and messages [3]. Researchers have recognized that every person has a personality that usually remains consistent over time. Consequently, personality assessment can be used as an important measure. Various psychological models of personality have been proposed, such as the Five-factor model [4], the psychoticism, extraversion, and neuroticism (PEN) model [5], the Myers-Briggs type inventory [4], and the dominance, influence, steadiness, and compliance (DISC) model [6]. Typically, these models propose direct methods such as questionnaires to recognize personality. Conversely, linguistic analysis can be used to detect personality [3, 7]. Linguistic analysis can produce useful patterns for establishing relationships between writingcharacteristics and personality. Researchers in natural language processing have proposed several methods of linguistic analysis to recognize personality, and machine learning has been one of the most investigated approaches. Machine learning techniques are useful in the recognition of personality since they provide mechanisms to automatize processes that are based on a set of examples. Several proposals for personality recognition based on machine learning can be found in the literature [8; 9]. Machine learning algorithms use computational methods to learn directly from data without relying on a predetermined equation as a model. The algorithms adaptively improve their performance as the number of instances available for learning increases [10]. Several efforts in personality prediction from the linguistic analysis approach have been carried out. However, they have focused mostly on the English language and are based on the five-factor model. This model (also called big five model) has been used as a standard for applications that need personality modeling [7]. To contribute to the advancement and understanding of the relationship between personality and language, we have developed a predictive model for personality recognition based on the DISC personality model and a machine learning approach. We performed a personality survey with 120 participants. The participants were asked to complete a demographic form, fill in the DISC test, and handwrite a text on a general topic that they selected. The model for personality prediction is based on a supervised machine learning approach for multiclass classification. We evaluated six of the most known classifiers: naive Bayes [11], sequential minimal optimization (SMO) [12], k-Nearest neighbors (kNN) [13], AdaBoost [14], 4J8 [15], and random forest [16]. We conducted preprocess tasks as feature extraction, feature selection and data augmentation to have nine versions of the dataset. We found AdaBoost [14] and random forest [16] had the best performance. Figure 1 presents the overview of our approach. This paper presents the construction of the predictive model for personality recognition. Section 1 presents related work and background.