Sistemas de Geração de Língua Natural tentam produzir textos de maneira automatizada. Em sistemas desse tipo, é desejável produzir textos de forma realista - ou psicologicamente plausível - como forma de aumentar o engajamento do leitor. Uma das formas de alcançar esse objetivo é gerando textos de modo a refletir uma personalidade-alvo de interesse. Por exemplo, uma pessoa extrovertida usaria palavras mais simples e seus textos teriam mais interjeições e traços de oralidade. Esse trabalho tem o objetivo de desenvolver um modelo de reescrita sentencial para o português brasileiro com base em traços de personalidade de um locutor-alvo. Para isso, foi coletado um córpus de textos e inventários de personalidade e, com base em uma análise preliminar desses dados, foram encontrados fortes indícios de correlação entre os fatores de personalidade e as características observadas dos textos em português brasileiro. Foram gerados três modelos de lexicalização, referentes à adjetivos, substantivos e verbos. Esses modelos de lexicalização, então, foram utilizados na proposta de um modelo de reescrita sentencial para selecionar as palavras mais adequadas à personalidade-alvo. Os resultados demonstram que o uso de personalidade permite que o texto gerado seja mais próximo do desempenho humano se comparado a um sistema de baseline que faz escolhas lexicais mais frequentes / Natural Language Generation Systems attempt to produce texts in an automated fashion. In systems of this kind, it is desired to produce texts realisticaly - or at least psychologically plausible - as a way to increase reader\'s engagement. One way to achieve this goal is generating texts in such a way to reflect a target personality profile. For example, an extroverted individual would use simpler words and its texts would have more interjections and orality traces. This work proposes the development of a Brazilian Portuguese personality-based sentence rewrite model. To this end, a corpus with text samples and personality inventories has been collected, and, based on a preliminary analysis, strong correlations between personality and text features have been found. Three lexicalization models were generated, related to adjectives, nouns and verbs. These models were then used by the sentence rewrite model to select the most appropriate word for the target personality. Results show that the usage of personality allows the generated text to be closer to human performance when compared to a baseline system that makes lexical choices based on frequency

O presente trabalho aborda a determinação de conteúdo na fase de planejamento do documento no pipeline de Geração de Língua Natural (GLN) usando fatores de personalidade do modelo dos Cinco Grandes Fatores (CGF). O principal objetivo deste trabalho é gerar modelos computacionais de determinação de conteúdo baseados nos fatores de personalidade CGF. O trabalho aplicará técnicas já existentes de GLN para determinação de conteúdo, levando em conta os fatores de personalidade mapeados pelo modelo CGF. São utilizados os conceitos descritos por substantivos e os conceitos descritos por adjetivos relacionados aos substantivos na tarefa de descrição de cenas para a determinação de conteúdo. As principais contribuições desse trabalho são uma investigação de se e como a determinação de conteúdo de descrições textuais é influenciada pela personalidade do autor, além de entregar um modelo de determinação de conteúdo baseado em personalidade para os conceitos em que o trabalho foi aplicado, entre outras entregas. É apresentado o embasamento teórico com os conceitos fundamentais de GLN, e mais especificamente de determinação de conteúdo, foco dessa pesquisa. Além disso, são apresentados os modelos de personalidade humana, com destaque ao modelo CGF e inventários CGF, utilizados para a coleta de dados e execução dessa proposta. São apresentados também os principais trabalhos relacionados à GLN e modelo CGF, mesmo que não tratem especificamente da influência dos CGF na determinação de conteúdo. Um experimento para coleta do córpus utilizado na pesquisa é descrito, e também os modelos para determinação de conteúdo no âmbito de conceitos representando entidades visuais e seus predicados, assim como a avaliação desses modelos. Por fim, são apresentadas as conclusões obtidas com os modelos desenvolvidos e experimentos realizados / The present research approaches the content determination in the document planning phase of the Natural Language Generation (NLG) pipeline using personality factors of the Big Five Factor (BFF) model. The main objective of this research is to generate computational models of content determination based on the BFF personality factors. This research will apply existing NLG models to the content determination, taking into account the personality factors mapped by the BFF model. The concepts described by nouns and the concepts described by adjectives related to nouns in the task of describing scenes for content determination are used. The main contributions of this research are an investigation of if and how the content determination of textual descriptions is influenced by the personality of the author, in addition to providing a personality-based content determination model for the concepts in which the research was applied, among others deliveries. This document presents the theoretical basis and the fundamental NLG concepts, and more specifically the concept of content determination, which is the focus of this research. In addition, human personality models are presented, with emphasis on the BFF model and BFF inventories, used both for data collection and development of this proposal. The main studies related to NLG and the BFF model, even if they do not specifically address the influence of BFF in content determination, are also presented. An experiment for collecting the corpus used in the research is described, also the models to determine content in the scope of concepts representing visual entities and their predicates, as well as an evaluation of these models. Finally, the conclusions obtained with the developed models and experiments are presented

The present study aims to predict which individual factors may influence strategy-choices in wayfinding situations, specifically when participants are faced with ambiguous instructions in unfamiliar environments. Individual differences were measured with self-report forms of the Big Five personality traits and the Santa Barbara sense of direction scale (SBSOD). The study was conducted in a web-based survey format with a n=104 (65 female, and 39 male). A regression analysis concluded that the trait conscientiousness was the only factor that had predictive value in determining choice of strategy. SBSOD had some predictive values towards strategy-choice, but needs further investigation before any general conclusion can be drawn. Future studies should focus on a more goal‑oriented task with more realistic stimulus.

O presente trabalho aborda a determinação de conteúdo na fase de planejamento do documento no pipeline de Geração de Língua Natural (GLN) usando fatores de personalidade do modelo dos Cinco Grandes Fatores (CGF). O principal objetivo deste trabalho é gerar modelos computacionais de determinação de conteúdo baseados nos fatores de personalidade CGF. O trabalho aplicará técnicas já existentes de GLN para determinação de conteúdo, levando em conta os fatores de personalidade mapeados pelo modelo CGF. São utilizados os conceitos descritos por substantivos e os conceitos descritos por adjetivos relacionados aos substantivos na tarefa de descrição de cenas para a determinação de conteúdo. As principais contribuições desse trabalho são uma investigação de se e como a determinação de conteúdo de descrições textuais é influenciada pela personalidade do autor, além de entregar um modelo de determinação de conteúdo baseado em personalidade para os conceitos em que o trabalho foi aplicado, entre outras entregas. É apresentado o embasamento teórico com os conceitos fundamentais de GLN, e mais especificamente de determinação de conteúdo, foco dessa pesquisa. Além disso, são apresentados os modelos de personalidade humana, com destaque ao modelo CGF e inventários CGF, utilizados para a coleta de dados e execução dessa proposta. São apresentados também os principais trabalhos relacionados à GLN e modelo CGF, mesmo que não tratem especificamente da influência dos CGF na determinação de conteúdo. Um experimento para coleta do córpus utilizado na pesquisa é descrito, e também os modelos para determinação de conteúdo no âmbito de conceitos representando entidades visuais e seus predicados, assim como a avaliação desses modelos. Por fim, são apresentadas as conclusões obtidas com os modelos desenvolvidos e experimentos realizados / The present research approaches the content determination in the document planning phase of the Natural Language Generation (NLG) pipeline using personality factors of the Big Five Factor (BFF) model. The main objective of this research is to generate computational models of content determination based on the BFF personality factors. This research will apply existing NLG models to the content determination, taking into account the personality factors mapped by the BFF model. The concepts described by nouns and the concepts described by adjectives related to nouns in the task of describing scenes for content determination are used. The main contributions of this research are an investigation of if and how the content determination of textual descriptions is influenced by the personality of the author, in addition to providing a personality-based content determination model for the concepts in which the research was applied, among others deliveries. This document presents the theoretical basis and the fundamental NLG concepts, and more specifically the concept of content determination, which is the focus of this research. In addition, human personality models are presented, with emphasis on the BFF model and BFF inventories, used both for data collection and development of this proposal. The main studies related to NLG and the BFF model, even if they do not specifically address the influence of BFF in content determination, are also presented. An experiment for collecting the corpus used in the research is described, also the models to determine content in the scope of concepts representing visual entities and their predicates, as well as an evaluation of these models. Finally, the conclusions obtained with the developed models and experiments are presented

Sistemas de Geração de Língua Natural tentam produzir textos de maneira automatizada. Em sistemas desse tipo, é desejável produzir textos de forma realista - ou psicologicamente plausível - como forma de aumentar o engajamento do leitor. Uma das formas de alcançar esse objetivo é gerando textos de modo a refletir uma personalidade-alvo de interesse. Por exemplo, uma pessoa extrovertida usaria palavras mais simples e seus textos teriam mais interjeições e traços de oralidade. Esse trabalho tem o objetivo de desenvolver um modelo de reescrita sentencial para o português brasileiro com base em traços de personalidade de um locutor-alvo. Para isso, foi coletado um córpus de textos e inventários de personalidade e, com base em uma análise preliminar desses dados, foram encontrados fortes indícios de correlação entre os fatores de personalidade e as características observadas dos textos em português brasileiro. Foram gerados três modelos de lexicalização, referentes à adjetivos, substantivos e verbos. Esses modelos de lexicalização, então, foram utilizados na proposta de um modelo de reescrita sentencial para selecionar as palavras mais adequadas à personalidade-alvo. Os resultados demonstram que o uso de personalidade permite que o texto gerado seja mais próximo do desempenho humano se comparado a um sistema de baseline que faz escolhas lexicais mais frequentes / Natural Language Generation Systems attempt to produce texts in an automated fashion. In systems of this kind, it is desired to produce texts realisticaly - or at least psychologically plausible - as a way to increase reader\'s engagement. One way to achieve this goal is generating texts in such a way to reflect a target personality profile. For example, an extroverted individual would use simpler words and its texts would have more interjections and orality traces. This work proposes the development of a Brazilian Portuguese personality-based sentence rewrite model. To this end, a corpus with text samples and personality inventories has been collected, and, based on a preliminary analysis, strong correlations between personality and text features have been found. Three lexicalization models were generated, related to adjectives, nouns and verbs. These models were then used by the sentence rewrite model to select the most appropriate word for the target personality. Results show that the usage of personality allows the generated text to be closer to human performance when compared to a baseline system that makes lexical choices based on frequency

O presente trabalho traz um estudo no âmbito de Geração de Língua Natural, com ênfase na tarefa de Geração de Expressões de Referência (GER), a qual consiste em gerar expressões referenciais semelhantes às produzidas por humanos. Existem estudos que exploram o uso da variação individual do ser humano no aprendizado do padrão de seleção de conteúdo na construção de descrições, contudo, treinar tais conjuntos de dados é computacionalmente caro. O trabalho apresenta um modelo de seleção de conteúdo para GER, baseado em traços de personalidade, o qual generaliza padrões de comportamentos referenciais similares em cada perfil de personalidade. Na pesquisa também realizou-se um levantamento bibliográfico sobre o tema, e construiu-se um córpus com expressões de referência contendo informações de personalidade de cada participante, as quais foram anotadas tomando por base o modelo dos Cinco Grandes Fatores. Este córpus tem como finalidade ser utilizado como entrada tanto no modelo desenvolvido, como em outros estudos na área. Os resultados comprovam que modelos de GER dependentes da personalidade superam os algoritmos GER tradicionais, e que são uma alternativa viável em abordagens que dependam da variação de locutores / The present work presents a study in the field of Generation of Natural Language, with emphasis on the task of Generation of Reference Expressions (GER), which is to generate reference expressions similar to those produced by humans. There are studies that explore the use of individual human variation in learning the pattern of content selection in the construction of descriptions, however, training such datasets is computationally expensive. The paper presents a content selection model for GER based on personality traits, which generalizes patterns of similar referential behavior in each personality profile. The research also carried out a bibliographic survey on the subject, and a corpus was constructed with reference expressions containing personality information of each participant, which were annotated based on the model of the Five Great Factors. This corpus is intended to be used as an input in both the developed model and other studies in the area. The results show that personality-dependent GER models exceed traditional GER algorithms, and are a viable alternative in approaches that depend on the variation of speakers

Social psychology has a long tradition of studying the personality traits associated with subjective well-being (SWB). However, research often depends on a priori but unempirical assumptions about how to (a) measure the constructs, and (b) mitigate confounded associations. These assumptions have caused profligate and often contradictory findings. To remedy, I demonstrate how a computational psychology paradigm-predicated on large online data and iterative analyses-might help isolate more robust personality trait associations. At the outset, I focussed on univariate measurement. In the first set of studies, I evaluated the extent researchers could measure psychological characteristics at scale from online behaviour. Specifically, I used a combination of simulated and real-world data to determine whether predicted constructs like big five personality were accurate for specific individuals. I found that it was usually more effective to simply assume everyone was average for the characteristic, and that imprecision was not remedied by collapsing predicted scores into buckets (e.g. low, medium, high). Overall, I concluded that predictions were unlikely to yield precise individual-level insights, but could still be used to examine normative group-based tendencies. In the second set of studies, I evaluated the construct validity of a novel SWB scale. Specifically, I repurposed the balanced measure of psychological needs (BMPN), which was originally designed to capture the substrates of intrinsic motivation. I found that the BMPN robustly captured (a) dissociable experiences of suffering and flourishing, (b) more transitive SWB than the existing criterion measure, and (c) unique variation in real-world outcomes. Thus, I used it as my primary outcome. Then, I focussed on bivariate associations. The third set of studies extracted pairs of participants with similar patterns of covarying personality traits-and differing target traits-to isolate less-confounded SWB correlations. I found my extraction method-an adapted version of propensity score matching-outperformed even advanced machine learning alternatives. The final set of studies isolated the subset of facets that had the most robust associations with SWB. It combined real-world surveys with a total of eight billion simulated participants to find the traits most prevalent in extreme suffering and flourishing. For validation purposes, I first found that depression and cheerfulness-the trait components of SWB-were highly implicated in both suffering and flourishing. Then, I found that self-discipline was the only other trait implicated in both forms of SWB. However, there were also domain-specific effects: anxiety, vulnerability and cooperation were implicated in just suffering; and, assertiveness, altruism and self-efficacy were implicated in just flourishing. These seven traits were most likely to be the definitive, stable, drivers of SWB because their effects were totally consistent across the full range of intrapersonal contexts.

Research on vocational behavior has made progress in identifying broad personality traits associated with career indecision; however, important questions remain unanswered about the temporal stability of relationships between broad personality traits and Career Decidedness (CD), and about the role of narrow personality traits as predictors of CD, both of which were addressed in this longitudinal field study. A total of 2,046 undergraduate students completed an online personality inventory and CD questionnaire. A sub-group (N=267) responded to a follow-up questionnaire seven months later. Results indicated, as hypothesized, that CD correlated positively with the broad (Big Five) personality traits, openness, conscientiousness, and agreeableness. However, CD did not correlate as expected with the broad trait, extraversion, and correlated significantly and inversely with the broad trait, neuroticism, only for low-achievement students. Results showed that the narrow traits of optimism and work drive correlated significantly and positively with CD, and that these narrow traits accounted for more variance in CD (11.2%) than broad traits (.5%). CD correlated positively with chronological age, as predicted. However, Career Decidedness only increased through the first three of four years of college, and contrary to predictions, showed a non-significant decline in the senior year. In an unexpected finding based only on the sub-group who completed the second set of questionnaires, the relationship of personality and CD strengthened over the 7-month span of this study, yet instability within CD warrants caution. Results suggest questions for future research and implications for practice in vocational psychology.

<p>The interpretation of this qualitative study indicates that individuals, teams and the organisation can benefit through allowing focus of unlocking that which provides a positive stimulus during challenging times in organisational settings. For social scientists, and in particular behavioural scientists, including industrial psychologists that are interested in positive psychology, it is hoped that there is an invitation to grow this area of research further and to gain new insights and direction for what are the enablers to experience positive change and gratitude.</p>

AbstractAuthor: Sofie JakobssonCourse: Media and Communication Studies CUniversity: Division of Media and Communication, Department of Information Science, Uppsala UniversityPeriod: Spring 2012Key words: Job ads, Professional Communicator, Public Relations Officer, Trait Theory, Five Factor Theory, Big Five, personal traits, work tasks.Title: The Independent Specialist. A study of requested personal types in Professional Communicators.Number of pages: 35Purpose/ Aim: The study aims to evaluate what kind of personality type that would make a good fit for a Professional Communicator. The study examines personal traits and work tasks that are presented in job ads for Professional Communicators in Sweden today. From this material the author wants to calculate what kind of personal type that characterize the profession with the help of the Big Five theory.Material/ Method: Quantitative research method based on job ads.Main results: The study shows that personal traits that employers look for in Professional Communicators today are mainly traits from the Conscientiousness group with characteristics such as independent, structured and planning. The second biggest personality group is Extraversion where personal traits like driving, outgoing and social were put. The results also indicate that the professional should enjoy working independently but also be able to cooperate with other people. A big part of the profession involves tasks which are of a producing nature which indicates that a talent or personality that fits for that kind of tasks is to prefer.