A System for Personality and Happiness Detection

This work proposes a platform for estimating personality and happiness. Starting from Eysenck's theory about human's personality, authors seek to provide a platform for collecting text messages from social media (Whatsapp), and classifying them into different personality categories. Although there is not a clear link between personality features and happiness, some correlations between them could be found in the future. In this work, we describe the platform developed, and as a proof of concept, we have used different sources of messages to see if common machine learning algorithms can be used for classifying different personality features and happiness

OpinAIS: An Artificial Immune System-based Framework for Opinion Mining

This paper proposes the design of an evolutionary algorithm for building classifiers specifically aimed towards performing classification and sentiment analysis over texts. Moreover, it has properties taken from Artificial Immune Systems, as it tries to resemble biological systems since they are able to discriminate harmful from innocuous bodies (in this case, the analogy could be established with negative and positive texts respectively). A framework, namely OpinAIS, is developed around the evolutionary algorithm, which makes it possible to distribute it as an open-source tool, which enables the scientific community both to extend it and improve it. The framework is evaluated with two different public datasets, the first involving voting records for the US Congress and the second consisting in a Twitter corpus with tweets about different technology brands, which can be polarized either towards positive or negative feelings; comparing the results with alternative machine learning techniques and concluding with encouraging results. Additionally, as the framework is publicly available for download, researchers can replicate the experiments from this paper or propose new ones

Recent Trends in Deep Learning Based Personality Detection

Recently, the automatic prediction of personality traits has received a lot of attention. Specifically, personality trait prediction from multimodal data has emerged as a hot topic within the field of affective computing. In this paper, we review significant machine learning models which have been employed for personality detection, with an emphasis on deep learning-based methods. This review paper provides an overview of the most popular approaches to automated personality detection, various computational datasets, its industrial applications, and state-of-the-art machine learning models for personality detection with specific focus on multimodal approaches. Personality detection is a very broad and diverse topic: this survey only focuses on computational approaches and leaves out psychological studies on personality detection

Faktor kesihatan mental, kecerdasan spiritual dan demografi ke atas kebahagiaan hidup dan prestasi akademik pelajar

Penyelidikan ini bertujuan untuk mengkaji pengaruh faktor kesihatan mental, kecerdasan spiritual dan demografi ke atas kebahagiaan hidup dan prestasi akademik pelajar-pelajar Ijazah Sarjana Muda di Universiti Malaysia Pahang (UMP). Objektif kajian secara khususnya adalah untuk: (1) mengenal pasti tahap kesihatan mental, tahap kecerdasan spiritual dan tahap kebahagiaan hidup pelajar UMP; (2) menilai pengaruh kesihatan mental dan kecerdasan spiritual terhadap kebahagiaan hidup; (3) menilai pengaruh kebahagiaan hidup terhadap prestasi akademik; (4) mengkaji pengaruh kebahagiaan hidup sebagai faktor mediasi di antara kesihatan mental dan kecerdasan spiritual dengan prestasi akademik; dan (5) menilai perbezaan pada kebahagiaan hidup berdasarkan demografi jantina, tahun pengajian dan agama. Kajian dijalankan secara kuantitatif melalui kaedah tinjauan dengan menggunakan soalselidik sebagai instrumen kajian ke atas 460 orang responden yang dipilih melalui teknik persampelan rawak mudah daripada 9121 orang jumlah keseluruhan pelajar Ijazah Sarjana Muda UMP. Analisis deskriptif mendapati 40.2% responden menunjukkan petanda masalah kesihatan mental dengan skor purata GHQ-30 melebihi titik potong (M=6.65). Walau bagaimanapun, majoriti responden didapati mempunyai tahap kecerdasan spiritual (M=3.92) dan tahap kebahagiaan hidup (M=3.82) yang agak tinggi. Analisis korelasi pula menunjukkan bahawa kesihatan mental dan kecerdasan spiritual mempunyai pengaruh yang signifikan ke atas kebahagiaan hidup dan prestasi akademik. Analisis regresi membuktikan bahawa kebahagiaan hidup tidak mempunyai pengaruh mediasi yang signifikan ke atas hubungan di antara kesihatan mental dengan prestasi akademik. Walau bagaimanapun, kebahagiaan hidup bertindak sebagai mediator separuh ke atas hubungan di antara kecerdasan spiritual dan prestasi akademik. Ujian t dan ANOVA pula mendapati tahap kebahagiaan pelajar berbeza mengikut jantina tetapi tidak dipengaruhi oleh faktor tahun pengajian dan agama. Secara keseluruhannya, kajian ini telah menyumbang kepada kekurangan dalam literatur mengenai tahap kesihatan mental, kecerdasan spiritual dan kebahagiaan hidup dalam konteks pelajar universiti di Malaysia khususnya di UMP. Hasil kajian juga dilihat mengukuhkan dapatan teori lepas mengenai kaitan di antara faktor kesihatan mental, kecerdasan spiritual dan demografi (jantina, tahun pengajian dan agama) dengan kebahagiaan hidup serta prestasi akademik pelajar. Selain itu, model yang dibina dalam kajian ini boleh dijadikan rujukan untuk memahami bagaimana kebahagiaan hidup dan prestasi akademik pelajar boleh ditingkatkan melalui amalan gaya hidup yang boleh meningkatkan kesihatan mental dan kekuatan rohani mereka

Modelo computacional e sua implementação para identificação de perfil de personalidade baseado em textos educacionais

Orientador: Prof. Dr. Andrey Ricardo PimentelTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 14/09/2018Inclui referências: p.132-144Área de concentração: Ciência da ComputaçãoResumo: A identificacao do perfil de personalidade de alunos, levando em consideracao as diferencas, colabora com os educadores no processo de encontrar situacoes de aprendizagem adequadas para cada aluno. Este processo pode ser realizado de forma intuitiva em pequenas turmas presenciais, mas apresenta-se como um grande desafio no cenario de grandes turmas em ambientes a distancia. Uma das formas de identificar o perfil de personalidade e a utilizacao dos inventarios de personalidade, nos quais os alunos respondem a uma serie de perguntas que sao posteriormente avaliadas, gerando os indicadores de perfil de personalidade de acordo com um modelo especifico. Em contrapartida a esses metodos manuais de aplicacao de inventarios, tem-se desenvolvido metodos nao intrusivos, baseados, por exemplo, na identificacao das pistas de personalidade registradas pelos individuos nos textos por estes produzidos. Com a utilizacao de processos de aprendizado de maquina, as pistas identificadas nos textos podem ser comparadas as pistas identificadas em bases de dados de referencia, nas quais um processo previo de identificacao manual foi realizado, inferindo-se assim o perfil de personalidade dos autores dos textos. Esta pesquisa apresenta um modelo, denominado IP3, que permite a realizacao da identificacao automatica do perfil de personalidade de alunos, de uma forma nao intrusiva, tendo como referencia somente o texto em portugues registrado por estes alunos em atividades educacionais. Este modelo e baseado em aprendizado de maquina, utilizando bases de aprendizado previamente rotuladas, modelos de representacao do texto e tecnicas de classificacao. Como base de treinamento e referencia para os testes dos classificadores, foram utilizadas as bases ESSAYS e myPersonality, bases estas utilizadas por diversas pesquisas na area de identificacao de personalidade a partir do texto. Para a representacao do texto foi utilizado o lexico LIWC, bem como a representacao estatistica nos modelos n-gram e Word2Vec. Tambem foram avaliadas as tecnicas utilizadas para classificacao de texto, sendo proposta a utilizacao da estrategia de combinacao de classificadores. Com o objetivo de validar o modelo apresentado, foi realizado um experimento pratico em um ambiente educacional. Os resultados apresentados demonstram a viabilidade da utilizacao do modelo IP3 para identificacao do perfil de personalidade dos alunos baseado somente nos textos registrados em ambientes educacionais. Palavras-chave: identificacao de personalidade, classificacao de texto, representacao de texto, processamento de linguagem natural, aprendizado de maquina.Abstract: The personality profile identification of students supports educators in the process of finding suitable learning conditions for each student while considering their differences. Although this process can be used in an intuitive way for small groups in classroom learning, it proves to be a significant challenge in the landscape of large distance learning groups. One way of identifying the personality profile is by using the personality inventory. By using this method, students answer a series of questions that are later evaluated, generating personality profile indicators according to a specific model. In contrast to that, we can find the use of non-intrusives methods. They are based on the identification of personality clues which can be derived from the text produced by individuals. With the use of machine learning processes, these clues are identified within the text and can be compared to the clues found in databases of reference, in which an inference of a personality profile has been identified, through a previous manual identification process. This research had the purpose of obtaining a model, named IP3, that allows the identification of students' profile in a non-intrusive way. It considered only text in portuguese produced by these students in their educational activities. To conduct this research, the author investigated text representation techniques that allowed to obtain clues about the writer. The methods used in this research were the LIWC lexicon as well as the statistic representation in the n-gram and Word2Vec models. Additionally, the classification and the classifiers combination specification techniques were also evaluated in the proposed model. As a training basis and reference for the classifiers' tests, ESSAYS and myPersonality databases have been used, which are commonly used by several researchers in the field of personality identification from text. To validate the model presented, a practical experiment was conducted in an educational environment. The presented results indicate the viability regarding the use of the IP3 student's personality profile identification model, based on the text produced by them during educational activities. Keywords: personality recognition, text classification, text representation, natural language processing, machine learning

A System for Personality and Happiness Detection.

This work proposes a platform for estimating personality and happiness. Starting from Eysenck's theory about human's personality, authors seek to provide a platform for collecting text messages from social media (Whatsapp), and classifying them into different personality categories. Although there is not a clear link between personality features and happiness, some correlations between them could be found in the future. In this work, we describe the platform developed, and as a proof of concept, we have used different sources of messages to see if common machine learning algorithms can be used for classifying different personality features and happiness