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| Topic Mining of Online Reviews Based on Gaussian Latent Dirichlet Allocation |
| Guo Xianda, Zhao Narisa, Gao Huan, Yang Xinyi |
| Institute of Systems Engineering, Dalian University of Technology, Dalian 116024 |
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Abstract This study proposes a method based on Gaussian latent Dirichlet allocation (LDA) for online comments to overcome the limitations of the current topic mining methods, such as sparseness and semantic incoherence of generated topics, that result in a poor applicability. The word vectors of online comments are obtained by word2vec training, and the topic distribution of online comments is achieved based on the Gaussian LDA model. The topic distribution is then used to calculate the similarity matrix of comments, and the affinity propagation clustering algorithm is employed to cluster online comments. The topic discovery is realized by analyzing the clustering results. Finally, the TextRank algorithm is used to extract the key sentences of each topic to generate the topic summary so that the description of the topic can be completed. The proposed method effectively alleviates the information overload problem of consumers online comments. The effectiveness and practical application value of the proposed method have been established through experiments and calculations performed on online product reviews from seven platforms, such as Taobao, Jingdong, and Douban.
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Received: 19 July 2019
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