Learning to Answer Psychological Questionnaire for Personality Detection (2021)

Abstract: 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.

## introduction as a psychological conception, personality aims to explain human behaviors in terms of a few stable and measurable individual characteristics @xcite . the study of personality is fundamental to psychology, and personality detection @xcite has benefited many applications such as dialogue systems @xcite , recommendation systems @xcite , and suicide risk assessment @xcite . canonical approaches to personality test are * corresponding author. recent years have witnessed an increasing interest in automatically identifying one's personality traits based on her/his social media posts @xcite @xcite . to encode the input posts and obtain their context representations, most of these methods employ deep learning models such as lstms @xcite , cnns @xcite and pre-trained language models (ptms) @xcite . they generally rely on the models to capture potential personality cues implicitly from the texts in a data-driven manner, without any guidance of psychological domain knowledge. as a result, the performance of these models is largely limited by the availability of training data and the learning capability of models. we observe from real data that the posts created by a user contain some critical contents that could help answer the questions in a questionnaire. as the example shows in figure 1 , there are a set of posts from a user and a question "are you usually a good mixer with groups of people or rather quiet and reserved?" from an mbti @xcite questionnaire. the question is also associated with two choices "quite and reserved." and "a good mixer.", which are intended to investigate whether the user's personality trait is introversive (the former) or extroversive (the latter). from the posts, we can see that the contents "always been very reserved", "need time alone" and "better to be single" strongly indicate that the user's personality trait is introversive. therefore, we argue that it is possible to utilize the questionnaire, which contains questions that are highly related to personality traits, to guide a model to capture critical information in the posts for personality detection. for this purpose, we propose a new model named psychological questionnaire enhanced network (pq-net) for text-based personality detection. specifically, pq-net consists of two streams: a context stream and a questionnaire stream. for the context stream, a ptm-based encoder is employed to encode each post and create its contextual representation. for the questionnaire stream, it first encodes each question by a question encoder and each candidate answer by a choice encoder, and then employs a cross-attention mechanism with supervision to enable the model to learn a potential answer representation for each question by choosing the correct answer based on the post representations. we then concatenate the post representations and the potential answer representations to predict the user's personality traits. under the guidance of the questionnaire, our pq-net is able to capture personality-related cues from the posts in an explicit manner rather than learning them implicitly. extensive experiments on the kaggle and pandora datasets show that pq-net consistently outperforms existing competitors with superior performance. further analyses also demonstrate that the questionnaire and the two-streams structure all play a crucial role in pq-net, and that the user representations enhanced by pq-net are more inductive and distinguishable in comparison to the baselines. lastly, we show that the cues obtained by pq-net are more interpretable for personality detection. the contributions of this paper are threefold: ## experiments in this section, we first introduce the details of the personality benchmarks, questionnaire and baseline models adopted in our study, and then report and discuss our experimental results. ## related work in recent years, numerous efforts have been devoted to automatically detecting one's personality from his/her online texts @xcite @xcite . the early works rely on hand-crafted features @xcite @xcite ; amirhosseini and kazemian, 2020), which include various psycholinguistic features extracted by liwc and statistical features extracted by bag-of-words models @xcite . nevertheless, feature engineering-based methods are limited by their capability in extracting many useful implicit features @xcite . meanwhile, deep neural networks have been applied to personality detection by implicitly extracting features from the texts @xcite . for example, @xcite and @xcite applied lstms to encode each post with the glove embeddings @xcite employed bert to encode each post. moreover, hierarchical structures were also applied to merge the texts into a user representation. for example, @xcite first encoded each post via a gated recurrent unit (gru) @xcite with word attention, and then passed the encodings to a second gru with post attention to aggregate the posts. @xcite designed an inception @xcite based attrcnn module to encode each post and then applied a convolutional neural network (cnn) @xcite interact between posts. despite numerous success, deep neural-network solutions rely on a data-driven fashion and lack the guidance of psychological domain knowledge. ## conclusion in this paper, we proposed a psychological questionnaire enhanced network (pq-net) for personality detection. pq-net aims to track personalityrelated cues from online posts in an explicit manner by considering the connections between the posts and a psychological questionnaire. specifically, pq-net comprises a context stream and a questionnaire stream. the former encodes each post to obtain its contextual representation, and the latter learns to capture critical information in the posts to result in a potential answer representation for each question in the questionnaire. finally, the potential answer representations are used to enhance the contextual post representations to predict the personality traits. experimental results on two benchmarks show that pq-net outperforms the baselines significantly. besides, further studies and analyses demonstrate that the representations enhanced by pq-net are more inductive and distinguishable, providing an interpretability for the personality detection process. data and have not illegally detected any user privacy information. we realize that the evaluation results in this paper may have certain ethical risks and thus require that they should be used in a strictly controlled manner, subject to the approval of the institutional review board. anyone who uses our work to secretly infer people's personality characteristics is strictly prohibited. tal yarkoni. 2010. personality in 100,000 words: a large-scale analysis of personality and word use among bloggers. journal of research in personality, 44(3):363-373. yin zhang, rong jin, and zhi-hua zhou. 2010. understanding bag-of-words model: a statistical framework. international journal of machine learning and cybernetics, 1(1-4):43-52. yinhe zheng, guanyi chen, minlie huang, song liu, and xuan zhu. 2019. personalized dialogue generation with diversified traits. arxiv preprint arxiv:1901.09672.