Based on the original text information, this paper converts the users' theme preferences and text sentiment features into attention information and combines different forms with the LSTM (Long Short-Term Memory) model to predict the personality characteristics of social network users. Finally, the experimental results of multiple groups’ show that the Attention-based LSTM model proposed in the paper can achieve better results than the currently popular methods in the recognition of user personality traits and that the model has good generalization ability.

中文翻译：

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基于主题偏好和基于LSTM模型的情感分析的用户个性预测

本文基于原始文本信息，将用户的主题偏好和文本情感特征转换为注意力信息，并将不同形式与LSTM（长短期记忆）模型相结合，以预测社交网络用户的个性特征。最后，多组实验结果表明，本文提出的基于注意力的LSTM模型在识别用户个性特征方面比目前流行的方法具有更好的效果，并且该模型具有良好的泛化能力。