This paper firstly describes the research status of online review text mining and finds out the problems existing in the mining and application of tourism texts. Aiming at these problems, this paper proposes a text mining method for tourism online reviews based on natural language processing and text classification technology. The first step is to analyze the validity of the online review text; the purpose is to remove the invalid text and improve the mining efficiency of the online review text. The second step is to conduct a comprehensive evaluation of scenic spots and hotels based on text classification technology and sentiment analysis. The comprehensive evaluation indicators are established for the five core service contents. High-quality scenic spots and hotels are selected according to the ranking of comprehensive evaluation. The third step is to propose a mining method of tourism hot words based on natural language processing for the selected high-quality tourist locations. The obtained hot words can intuitively show the impression of tourists on the scenic spot. The fourth step is to use mutual information combined with the left and right entropy to discover new words and to mine service characteristics of high-quality scenic spots and hotel from the new words. Finally, the proposed new methods are tested on the crawled tourism online review texts. The experimental results show that the novel comprehensive evaluation method proposed in this paper can truly and objectively select high-quality scenic spots and hotels and provide an important basis for the decision-making of tourism management. On this basis, hot words and new words can be effectively excavated from relevant online review texts, and travel impressions can be fed back from various aspects and angles.
Abnormal conditions are hazardous in process complex systems, and the aim of condition diagnosis is to detect abnormal conditions and thus avoid serious accidents. Comparing with conventional techniques of condition diagnosis without concerning the nonlinearity of complex system, multifractal analysis elaborately reveals scale-invariance or selfsimilarity properties of time series data, which is one of the intrinsic characteristics of complex systems. Moreover, the monitoring data within multiple feature variables should be investigated by combining multifractal analysis and information fusion techniques, so that significant patterns of the whole system would be discovered. In this article, a condition diagnosis framework is proposed for industrial complex systems, by which nonlinear features are extracted from univariate time series through multifractal analysis using multifractal detrended fluctuation analysis algorithm, and multiple feature variables are investigated through Mahalanobis-Taguchi system as an information fusion method to determine the condition of the whole system. The effectiveness of the approach is illustrated using data from both simulated model and real production system in an industrial enterprise.
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