Abstract
As an important medium for users to share experiences and feelings, social media is the carrier of users’ emotions, hobbies and interests, and it has become an important research content to mine user's personality information. Due to the unstructured and sparse characteristics of social texts, it is difficult to grasp the fine-grained word segmentation and contain a large number of noise words in mining user personality characteristics. However, the probability model of traditional language rules has weak generalization ability, and the deep learning model has poor anti-noise performance and poor feature extraction ability. This paper proposes a text analysis model based on attention mechanism, referred to as ATCNN-BiGRU. The model uses the attention mechanism to construct the input feature matrix of the text, solves the interference of noise in the social text, and integrates the double-layer gated unit network to overcome the problem that the fixed-step scanning mechanism in the convolutional neural network affects the extraction of global contextual semantic features. Through the experimental results on datasets in three different domains and different languages, it is found that compared with the existing mainstream text analysis models, the ATCNN-BiGRU proposed in this paper has a significant improvement in the prediction accuracy, especially in the Chinese data set by 2%.
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Acknowledgments
This paper is supported by the Inner Mongolia Natural Science Foundation Project (2020MS07018), the Graduate Research Innovation Project of Inner Mongolia University (11200–5223737), the National Natural Science Foundation of China (61862046) and the Inner Mongolia Autonomous Region Scientific and Technological Achievement Transformation Project (CGZH2018124).
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Zheng, Y., Shen, J., Jia, R., Li, R. (2023). Research on User Personality Characteristics Mining Based on Social Media. In: Sun, Y., et al. Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2022. Communications in Computer and Information Science, vol 1681. Springer, Singapore. https://doi.org/10.1007/978-981-99-2356-4_8
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DOI: https://doi.org/10.1007/978-981-99-2356-4_8
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