Multi-source network public opinion theme mining method based on improved hierarchical clustering
A technology of hierarchical clustering and source network, applied in text database clustering/classification, special data processing applications, unstructured text data retrieval, etc. It can solve the problem of lack of topic hierarchical mining, and achieve the effect of guiding the trend of network public opinion.
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[0040] Such as figure 1 As shown, the present invention provides a multi-source network public opinion topic mining method based on improved hierarchical clustering, which specifically includes the following steps:
[0041] Step 1. Acquisition of word vectors. The traditional vector space model constructs vectors through words and word frequency, without considering the relationship between contexts and semantic information; while most of the network platforms are short text, lack of vocabulary, noise and serious colloquialism, use the vector space model Vectorization cannot effectively represent the subject features while the vector dimension is high; correspondingly, the neural network language model uses neural networks to solve natural language processing problems in terms of text vector representation, and uses contextual relations and semantic information for training. A low-dimensional real-valued vector to represent words to avoid the curse of data dimensionality;
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