Myers-Briggs personality classification from social media text using pre-trained language models

• URL: http://arxiv.org/abs/2207.04476v1
• Date: Sun, 10 Jul 2022 14:38:09 GMT
• Title: Myers-Briggs personality classification from social media text using pre-trained language models
• Authors: Vitor Garcia dos Santos, Ivandr\'e Paraboni
• Abstract summary: We describe a series of experiments in which the well-known Bidirectional Representations from Transformers (BERT) model is fine-tuned to perform MBTI classification. Our main findings suggest that the current approach significantly outperforms well-known text classification models based on bag-of-words and static word embeddings alike.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In Natural Language Processing, the use of pre-trained language models has been shown to obtain state-of-the-art results in many downstream tasks such as sentiment analysis, author identification and others. In this work, we address the use of these methods for personality classification from text. Focusing on the Myers-Briggs (MBTI) personality model, we describe a series of experiments in which the well-known Bidirectional Encoder Representations from Transformers (BERT) model is fine-tuned to perform MBTI classification. Our main findings suggest that the current approach significantly outperforms well-known text classification models based on bag-of-words and static word embeddings alike across multiple evaluation scenarios, and generally outperforms previous work in the field.

Related papers

• Ensembling Finetuned Language Models for Text Classification [55.15643209328513]
  Finetuning is a common practice across different communities to adapt pretrained models to particular tasks. ensembles of neural networks are typically used to boost performance and provide reliable uncertainty estimates. We present a metadataset with predictions from five large finetuned models on six datasets and report results of different ensembling strategies.
  arXiv  Detail & Related papers  (2024-10-25T09:15:54Z)
• Fuzzy Fingerprinting Transformer Language-Models for Emotion Recognition in Conversations [0.7874708385247353]
  We propose to combine the two approaches to perform Emotion Recognition in Conversations (ERC) We feed utterances and their previous conversational turns to a pre-trained RoBERTa, obtaining contextual embedding utterance representations. We validate our approach on the widely used DailyDialog ERC benchmark dataset.
  arXiv  Detail & Related papers  (2023-09-08T12:26:01Z)
• Beyond Contrastive Learning: A Variational Generative Model for Multilingual Retrieval [109.62363167257664]
  We propose a generative model for learning multilingual text embeddings. Our model operates on parallel data in $N$ languages. We evaluate this method on a suite of tasks including semantic similarity, bitext mining, and cross-lingual question retrieval.
  arXiv  Detail & Related papers  (2022-12-21T02:41:40Z)
• Detecting Text Formality: A Study of Text Classification Approaches [78.11745751651708]
  This work proposes the first to our knowledge systematic study of formality detection methods based on statistical, neural-based, and Transformer-based machine learning methods. We conducted three types of experiments -- monolingual, multilingual, and cross-lingual. The study shows the overcome of Char BiLSTM model over Transformer-based ones for the monolingual and multilingual formality classification task.
  arXiv  Detail & Related papers  (2022-04-19T16:23:07Z)
• Pushing on Personality Detection from Verbal Behavior: A Transformer Meets Text Contours of Psycholinguistic Features [27.799032561722893]
  We report two major improvements in predicting personality traits from text data. We integrate a pre-trained Transformer Language Model BERT and Bidirectional Long Short-Term Memory networks trained on within-text distributions of psycholinguistic features. We evaluate the performance of the models we built on two benchmark datasets.
  arXiv  Detail & Related papers  (2022-04-10T08:08:46Z)
• Sentiment analysis in tweets: an assessment study from classical to modern text representation models [59.107260266206445]
  Short texts published on Twitter have earned significant attention as a rich source of information. Their inherent characteristics, such as the informal, and noisy linguistic style, remain challenging to many natural language processing (NLP) tasks. This study fulfils an assessment of existing language models in distinguishing the sentiment expressed in tweets by using a rich collection of 22 datasets.
  arXiv  Detail & Related papers  (2021-05-29T21:05:28Z)
• Extending the Abstraction of Personality Types based on MBTI with Machine Learning and Natural Language Processing [0.0]
  A data-centric approach with Natural Language Processing (NLP) to predict personality types based on the MBTI. The experimentation had a robust baseline of stacked models. The results showed that attention to the data iteration loop focused on quality, explanatory power and representativeness for the abstraction of more relevant/important resources.
  arXiv  Detail & Related papers  (2021-05-25T10:00:16Z)
• Personality Trait Detection Using Bagged SVM over BERT Word Embedding Ensembles [10.425280599592865]
  We present a novel deep learning-based approach for automated personality detection from text. We leverage state of the art advances in natural language understanding, namely the BERT language model to extract contextualized word embeddings. Our model outperforms the previous state of the art by 1.04% and, at the same time is significantly more computationally efficient to train.
  arXiv  Detail & Related papers  (2020-10-03T09:25:51Z)
• Grounded Compositional Outputs for Adaptive Language Modeling [59.02706635250856]
  A language model's vocabulary$-$typically selected before training and permanently fixed later$-$affects its size. We propose a fully compositional output embedding layer for language models. To our knowledge, the result is the first word-level language model with a size that does not depend on the training vocabulary.
  arXiv  Detail & Related papers  (2020-09-24T07:21:14Z)
• Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing [73.37262264915739]
  We show that for domains with abundant unlabeled text, such as biomedicine, pretraining language models from scratch results in substantial gains. Our experiments show that domain-specific pretraining serves as a solid foundation for a wide range of biomedical NLP tasks.
  arXiv  Detail & Related papers  (2020-07-31T00:04:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.