Using Meta-LSTM to Predict Personality Traits from Blog User Behaviors

Traditional methods for predicting personality traits can be time-consuming and expensive, but with the increasing availability of user-generated content on the web, there is potential for automatic data collection through web crawlers. In this paper, we propose a neural network model to predict the personality of blog users using user comments and click records. Our proposed method combines the Model-Agnostic Meta-Learning (MAML) classification model with the Long Short-Term Memory (LSTM) neural network to efficiently solve the long sequence recognition problem in deep learning. The text information gain and semantic features of user comments are used to classify user personality by the MAML model, and our approach is implemented using the PyTorch deep learning library. The paper provides an overview of traditional methods, machine learning, and meta-learning in predicting personality, and discusses the application of deep learning algorithms in various fields and their effectiveness in predicting personality. We evaluate the effectiveness of our proposed approach on a dataset of user-generated content from various blogs and achieve high accuracy in predicting personality traits using standard evaluation metrics. Our proposed approach has significant potential for applications in social media and user behavior analysis, providing a more efficient and cost-effective means of predicting personality traits.