A Method for MBTI Classification Based on Impact of Class Components

Cerkez, N.; Vrdoljak, Boris; Skansi, Sandro

doi:10.1109/access.2021.3121137

Cited by 12 publications

(2 citation statements)

References 35 publications

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“…This dataset maps the social media posts of the users to one of the 16 personality types. These 16 personality types have been characterized by four dichotomies which are Extraversion/Introversion (EI), Sensing/Intuition (SI), Thinking/Feeling (TF), and Judging/Perceiving (JP) [24]. The sample of the dataset listed in table 2 provides a complete idea about the MBTI dataset.…”

Section: Methodology a Dataset Analysis And Preparationmentioning

confidence: 99%

PersoNet: A Novel Framework for Personality Classification-Based Apt Customer Service Agent Selection

Sandra,

Prabowo,

Gaol

et al. 2024

IEEE Access

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Personality classification has garnered significant interest in psychology, computational social science, and Machine Learning (ML) due to its wide-ranging applications. This paper presents PersoNet, an innovative framework developed to identify personality types using the Myers-Briggs Type Indicator (MBTI), aimed at enhancing customer service experiences by matching customers with suitable support agents. PersoNet employs a Bidirectional Long Short-Term Memory (BiLSTM) neural network architecture and has achieved an impressive classification accuracy of over 93.98%. Our extensive experiments with the MBTI dataset reveal that the BiLSTM architecture effectively captures both temporal dependencies and semantic subtleties in textual data, contributing to this high level of accuracy. Consequently, PersoNet can accurately select customer service agents who match customer personalities, achieving a Customer Satisfaction Rate (CSR) of over 97.82%-a notable improvement of 20.25% in CSR based on our experimental data. These results establish PersoNet as a cutting-edge tool in personality classification, surpassing existing methods in both accuracy and computational efficiency and markedly enhancing customer service quality.

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Section: Methodology a Dataset Analysis And Preparationmentioning

confidence: 99%

PersoNet: A Novel Framework for Personality Classification-Based Apt Customer Service Agent Selection

Sandra,

Prabowo,

Gaol

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…With the development of the economy and the development of neural networks, it is necessary to combine neural networks and risk evaluation issues. At the same time, the model combining CNN and LSTM has been applied to text classi cation [21][22][23][24], image recognition [25][26][27][28], emotion analysis [21,29,30], and other elds. Based on the deep learning method of CNN and LSTM, this paper aims at studying the problem of the enterprise's credit score by using enterprise's behavior data.…”

Section: Introductionmentioning

confidence: 99%

The Evaluation on the Credit Risk of Enterprises with the CNN-LSTM-ATT Model

Zhang

2022

Computational Intelligence and Neuroscience

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Credit evaluation is a difficult problem in the process of financing and loan for small and medium-sized enterprises. Due to the high dimension and nonlinearity of enterprise behavior data, traditional logistic regression (LR), random forest (RF), and other methods, when the feature space is very large, it is easy to show low accuracy and lack of robustness. However, recurrent neural network (RNN) will have a serious gradient disappearance problem under long sequence training. This paper proposes a compound neural network model based on the attention mechanism to meet the needs of enterprise credit evaluation. The convolutional neural network (CNN) and the long short-term memory (LSTM) network were used to establish the model, using soft attention, the gradient propagates back to other parts of the model through the attention mechanism module. In the multimodel comparison experiment and three different enterprise data experiments, the CNN-LSTM-ATT model proposed in this paper is superior to the traditional models LR, RF, CNN, LSTM, and CNN-LSTM in most cases. The experimental results under multimodel comparison reflect the higher accuracy of the model, and the group test reflects the higher robustness of the model.

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A Bidirectional Encoder Representations from Transformers and CNN Based Prediction Model on Competitive Products

Chen,

Wang

2023

Lecture Notes in Networks and Systems

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A Method for MBTI Classification Based on Impact of Class Components

Cited by 12 publications

References 35 publications

PersoNet: A Novel Framework for Personality Classification-Based Apt Customer Service Agent Selection

PersoNet: A Novel Framework for Personality Classification-Based Apt Customer Service Agent Selection

The Evaluation on the Credit Risk of Enterprises with the CNN-LSTM-ATT Model

A Bidirectional Encoder Representations from Transformers and CNN Based Prediction Model on Competitive Products

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