Abstract
Capturing highly coherent topics from large-scale informal social media messages has always been a challenging task. Existing topic models fail to model the nonlinear dependence between text and structure, which is called the interference effect in quantum cognition. Therefore, we propose a Quantum-inspired Topic Model (QTM), which naturally admits a non-linear context composition. Specifically, based on the iterative-deepening random walks, we design a Crystal-like Structure Grid (CSG) to obtain the user structural features. Then, we propose a quantum density operator based network embedding. Such a density operator is essentially a nonlinear joint representation of the user structure and text information. The resulting user sequence embeddings are fed into the Neural Variational Inference (NVI) for topic detection. Extensive experimental results on three real-world microblog datasets demonstrate that QTM outperforms state-of-the-art models.
This work is funded in part by the National Key R&D Program of China (2017YFE0111900), the National Natural Science Foundation of China (61876129) and the European Unions Horizon 2020 research and innovation programme under the Marie Skodowska-Curie grant agreement No. 721321.
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- 1.
Weibo topic community is a collection of users who are interested in a common topic.
- 2.
All information about the user, including text and structural features.
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Tian, T., Hou, Y., Li, Z., Pan, T., Gao, Y. (2021). Socializing in Interference\(:\) Quantum-Inspired Topic Model with Crystal-Like Structure Grid for Microblog Topic Detection. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Communications in Computer and Information Science, vol 1516. Springer, Cham. https://doi.org/10.1007/978-3-030-92307-5_16
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