A novel approach to multi-attribute group decision-making based on interval-valued intuitionistic fuzzy power Muirhead mean

This paper focuses on multi-attribute group decision-making (MAGDM) course in which attributes are evaluated in terms of interval-valued intuitionistic fuzzy (IVIF) information. More explicitly, this paper introduces new aggregation operators for IVIF information and further proposes a new IVIF MAGDM method. The power average (PA) operator and the Muirhead mean (MM) are two powerful and effective information aggregation technologies. The most attractive advantage of the PA operator is its power to combat the adverse effects of ultra-evaluation values on the information aggregation results. The prominent characteristic of the MM operator is that it is flexible to capture the interrelationship among any numbers of arguments, making it more powerful than Bonferroni mean (BM), Heronian mean (HM), and Maclaurin symmetric mean (MSM). To absorb the virtues of both PA and MM, it is necessary to combine them to aggregate IVIF information and propose IVIF power Muirhead mean (IVIFPMM) operator and the IVIF weighted power Muirhead mean (IVIFWPMM) operator. We investigate their properties to show the strongness and flexibility. Furthermore, a novel approach to MAGDM problems with IVIF decision-making information is introduced. Finally, a numerical example is provided to show the performance of the proposed method

Sentiment Analysis of Tourism Online Reviews Using the Deep Learning Method Based on BiLSTM

The outbreak and spread of COVID-19 have a great impact on the tourism industry. In this paper, we focus on the cultural tourist attractions, and take the Palace Museum as an example to explore and analyze the sentiment from perspective of tourism management under the influence of the epidemic. Firstly, more than 40,000 online reviews before and during the epidemic are crawled from some well-known domestic tourism e-commerce platforms. Then, the deep learning method based on BiLSTM is used to establish the emotion polarity classifier, and the classifier has an accuracy rate of more than 80% on the test set. Afterwards, K-means algorithm is used for the dimension clustering of the review data, and combined with the tourism management factors, the specific and managerial dimension division is carried out. Finally, suggestions for the current epidemic management plan of the Palace Museum and feasible plans for future development are put forward, which can be used as a reference for other cultural tourist attractions