Comprehending international important Ramsar wetland documents using latent semantic topic model in kernel space

Abstract

The kernel‐based statistical semantic topic model is introduced for comprehending three species of internationally important Ramsar wetland documents describing the Lashi Lake wetland in the Yunnan Province, the Yancheng wetland in the Jiangsu Province, and the Zoige wetland in the Sichuan Province of China. Latent Dirichlet allocation (LDA) features are used to represent the semantic components of wetland documents. Kernel principal component analysis (KPCA) maps the topic components to the kernel space to attain the low dimensional principal components. Support vector machines (SVMs) are used to comprehend the semantic distribution of distinct wetland documents in the kernel space. The LDA+KPCA+SVM algorithm reaches 77.0% training and 75.9% test accuracy and 0.902 training and 0.840 test mean average precision scores in the application of comprehending the wetland documents, respectively. The performance of the proposed kernel‐based model is superior to the traditional models of LDA+SVM and LDA+PCA+SVM.

Online Latent Dirichlet Allocation Model Based on Sentiment Polarity Time Series

“…Xu et al [22][23][24] obtained the expression of topic words based on topic2vec, and then calculated the content intensity and sentimental tendency of the same topic through CNN, and analyzed the evolution of the topic. An et al [25][26][27][28] used word2vec and k-means for topic detection and adopted a multi-source sentiment analysis method based on sentiment dictionary to analyze the co-evolution of topic and sentiment. Liu et al [29,30] used the sentimental information of the previous moment as the priority of the current sentimental parameters in the topic model and used cross-entropy to calculate the sentimental similarity.…”