Personality prediction from social media posts using text embedding and statistical features

• Autorzy: Majima Seiyu , Markov Konstantin

Identyfikatory

DOI

10.15439/2022F133

• Konferencja: Federated Conference on Computer Science and Information Systems (17 ; 04-07.09.2022 ; Sofia, Bulgaria)
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

deep learning; sentiment analysis; social networking; system performance; bit error rate; predictive model; feature extraction

PL

głębokie uczenie; analiza nastrojów; sieci społecznościowe; wydajność systemu; bitowa stopa błędów; model predykcyjny; ekstrakcja cech

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2022
• Tom: Vol. 30
• Strony: 235--240
• Opis fizyczny: Bibliogr. 23 poz., il., tab., wykr.

Twórcy

autor

Majima Seiyu

• University of Aizu Aizuwakamatsu, Fukushima, Japan

autor

Markov Konstantin

• University of Aizu Aizuwakamatsu, Fukushima, Japan

Bibliografia

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• 21. L. Zhou, Z. Zhang, L. Zhao, and P. Yang, “Attention-based bilstm models for personality recognition from user-generated content,” Information Sciences, vol. 596, pp. 460–471, 2022.
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Uwagi

1. Track 4: 1st Workshop on Personalization and Recommender Systems

2. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).