Personality Prediction from Social Media Posts using Text Embedding and Statistical Features

Recent advances in deep learning based language models have boosted the performance in many downstream tasks such as sentiment analysis, text summarization, question answering, etc. Personality prediction from text is a relatively new task that has attracted researchers' attention due to the in...

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Bibliographic Details
Published in2022 17th Conference on Computer Science and Intelligence Systems (FedCSIS) Vol. 30; pp. 235 - 240
Main Authors Majima, Seiyu, Markov, Konstantin
Format Conference Proceeding Journal Article
LanguageEnglish
Published Polish Information Processing Society 01.01.2022
Subjects
Bit error rate
Deep learning
Feature extraction
Predictive models
Sentiment analysis
Social networking (online)
System performance
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ISSN2300-5963
DOI10.15439/2022F133

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Summary:Recent advances in deep learning based language models have boosted the performance in many downstream tasks such as sentiment analysis, text summarization, question answering, etc. Personality prediction from text is a relatively new task that has attracted researchers' attention due to the increased interest in personalized services as well as the availability of social media data. In this study, we propose a personality prediction system where text embeddings from large language models such as BERT are combined with multiple statistical features extracted from the input text. For the combination, we use the self-attention mechanism which is a popular choice when several information sources need to be merged together. Our experiments with the Kaggle dataset for MBTI clearly show that adding text statistical features improves the system performance relative to using only BERT embeddings. We also analyze the influence of the personality type words on the overall results.
ISSN:2300-5963
DOI:10.15439/2022F133