Personal Trait Analysis Using Word2vec Based on User-Generated Text

Personal trait is to measure the habitual patterns of behavior, thought, and emotion. It differs over individuals and is comparatively stable over time, relatively consistent over situations. Personal trait is significant for it has a lot of applications, such as recommendation system, chatbot and human resource management. It is convenient to recognize personal trait through wearable devices, social media and so on. Traditionally, personal trait is measured in general categories such as Big Five, which contains five traits: extroversion, neuroticism, agreeableness, conscientiousness, and openness. However, it is too abstract to describe personal trait in five aspects. We need the personal trait measured in more specific aspects, such as trait of interest or affect. We can know a person better through the traits in specific aspects than in the traditional abstract ways. In this paper, we proposed a general method of measuring personal trait called Personal Trait Matrix including topic word extraction and the word representation by word2vec based on user-generated text. Then a case study is made with datasets called myPersonality. The diversity of affects and social interactions are measured. Next, the correlation between the trait and the personality of Big Five was analyzed and discussed. The results demonstrate that the proposed method can measure the personal trait in affect and social interactions.

• Publication year

  2019

• Venue

  2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)

• Publication date

  2019-08-01

• Fields of study

  Computer Science

• Identifiers
  DOI 10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00213
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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