Personality Exploration System for Online Social Networks: Facebook Brands As a Use Case

Tareaf, Raad Bin; Berger, Philipp; Hennig, Patrick; Meinel, Christoph

doi:10.1109/wi.2018.00-76

Cited by 14 publications

(2 citation statements)

References 14 publications

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“…In recent years, numerous efforts have been devoted to automatically detecting one's personality from his/her online texts (Adamopoulos et al, 2018;Tareaf et al, 2018;Guan et al, 2020). The early works rely on hand-crafted features (Yarkoni, 2010;Schwartz et al, 2013;Cui and Qi, 2017; Amirhosseini and Kazemian, 2020), which include various psycholinguistic features extracted by LIWC and statistical features extracted by bag-of-words models (Zhang et al, 2010).…”

Section: Related Workmentioning

confidence: 99%

Learning to Answer Psychological Questionnaire for Personality Detection

Yang¹,

Yang²,

Su³

2021

Findings of the Association for Computational Linguistics: EMNLP 2021

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Existing text-based personality detection research mostly relies on data-driven approaches to implicitly capture personality cues in online posts, lacking the guidance of psychological knowledge. Psychological questionnaire, which contains a series of dedicated questions highly related to personality traits, plays a critical role in self-report personality assessment. We argue that the posts created by a user contain critical contents that could help answer the questions in a questionnaire, resulting in an assessment of his personality by linking the texts and the questionnaire. To this end, we propose a new model named Psychological Questionnaire enhanced Network (PQ-Net) to guide personality detection by tracking critical information in texts with a questionnaire. Specifically, PQ-Net contains two streams: a context stream to encode each piece of text into a contextual text representation, and a questionnaire stream to capture relevant information in the contextual text representation to generate potential answer representations for a questionnaire. The potential answer representations are used to enhance the contextual text representation and to benefit personality prediction. Experimental results on two datasets demonstrate the superiority of PQ-Net in capturing useful cues from the posts for personality detection.

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Section: Related Workmentioning

confidence: 99%

Learning to Answer Psychological Questionnaire for Personality Detection

Yang¹,

Yang²,

Su³

2021

Findings of the Association for Computational Linguistics: EMNLP 2021

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“…This was then fed to a supervised classifier. Raad Bin Tareaf et al [10] modelled a paper that utilized research that was done in the field of analysis of text and detection of personality to develop an 'automatic brand personality prediction model'. This was done based on the big five model, and features such as linguistic inquiry and word count were used from sources available publicly.…”

Section: Related Workmentioning

confidence: 99%

MBTI Based Personality Prediction o f a User Based o n Their Writing o n Social Media

Gupta¹,

Madhavan²,

Duvvuri³

2019

IJEAT

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The undeniable power that various e-commerce and streaming websites exert on their users’ in terms of what they buy and what they watch is unquestionable, so the creation of better targeted advertisements and recommender systems is the need of the hour. Prediction of a person’s personality can be the key for the achievement of these goals. A novel way to understand the various facets of a person’s personality is by analyzing their MBTI (Myers–Briggs Type Indicator). This paper aims at classifying a user into any one of the sixteen personality types, defined by MBTI, through the use of natural language processing (NLP) and support vector machine (SVM) which was implemented on the MBTI dataset. Since the original dataset is unevenly distributed, SVM has been applied to the original dataset and an under sampled version of the MBTI dataset. The highest accuracy rate of 78.52% for the traits (thinking/feeling) was achieved in the original dataset whereas for the under sampled dataset it was 60.2% for the traits (judging/perceiving).

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Amazon Product Brand Analysis Framework Using Apache Spark on Real-Time Consumer’s Perception

Nagdive

Tugnayat²

2021

Algorithms for Intelligent Systems

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Personality Exploration System for Online Social Networks: Facebook Brands As a Use Case

Cited by 14 publications

References 14 publications

Learning to Answer Psychological Questionnaire for Personality Detection

Learning to Answer Psychological Questionnaire for Personality Detection

MBTI Based Personality Prediction o f a User Based o n Their Writing o n Social Media

Amazon Product Brand Analysis Framework Using Apache Spark on Real-Time Consumer’s Perception

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