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    Please use this identifier to cite or link to this item: http://libir.tmu.edu.tw/handle/987654321/64321


    Title: Identifying Five-Factor Model of Personality through text with Natural Language Processing for Social Media Recruitment on Reddit
    Authors: JIE, LIEW DI
    Contributors: 大數據科技及管理研究所碩士班
    張詠淳
    Keywords: Recruitment;Natural Language Processing;Personality;Social Media;Text Mining;Deep Learning
    Date: 2023-06-20
    Issue Date: 2024-09-30 14:21:19 (UTC+8)
    Abstract: Recruitment is an essential function of Human Resources. Finding an appropriate hire is crucial to the organization. Understanding a candidate’s personality can be useful to the hiring process as employers can have an idea of whether the candidate fits the role and culture. The arrival of the internet and social media has brought large changes to recruitment, but they also bring new opportunities. Social media such as Twitter, Facebook and Reddit allow employers to market themselves to potential employees and candidates can engage directly with employers. These engagements produced a lot of text content which can be analyzed to find out more about candidates as it is possible to capture some aspects of their personality through their use and style of language.
    This research aims to apply machine learning and deep learning techniques to Reddit text data on the Five-Factor Model. The research will compare ten machine learning classifiers and six deep learning classifiers which includes two transformer models with our proposed sentence selection method and deep learning model architecture, FF-BERT. Our proposed method will utilize Log-Likelihood Ratio to extract keywords from the high and low end of each personality dimension and combine them to create a list of keywords which will be used to extract the most relevant sentences for the five personality dimensions which will be used for training. We also performed Topic Modeling using BERTopic and compared the results with keywords from each personality dimension.
    Our results showed that the proposed sentence selection method and deep learning architecture was able to achieve substantial gains compared to the machine learning and deep learning techniques. We also found some patterns within the topics extracted by BERTopic and keywords which match some characteristics of the personality dimensions.
    Description: 碩士
    指導教授：張詠淳
    口試委員：蘇家玉
    口試委員：張詠淳
    口試委員：陳建錦
    Note: 論文公開日期：2024-01-03
    Data Type: thesis
    Appears in Collections:[Graduate Institute of Data Science] Dissertations/Theses

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