Analysis of Emotional Tendency of Tourism Consumers Based on LSTM and Fuzzy Control Algorithm

Abstract Learning the emotional tendency of travelers improves their interests and provide optimal traveling recommendations. This, however, requires large volumes of data such as travel plans, visit sites, personal interests, value for money, etc. for a detailed analysis. For ease of such analysis, in this article, an organized combinational control method (OC2M) is proposed. This method relies on conventional long short-term memory (LSTM) and fuzzy control (FC) to support such analysis. The first is responsible for filtering non-repeated data from the previous travel/ tour history reducing the data discreteness. This is organized non-recurrently to prevent outdated/ trivial data from influencing the consumer’s emotional learning. The FC process filters the adaptable data with the future tour/ travel plan for providing optimal recommendations that are liable to the consumer’s emotional tendency. This is identified based on the user's interest, preferences, and emotional connectivity with the place or plan from previous histories. Therefore, the proposed method improves the recommendation and validation tendencies of the consumer’s travel plan accordingly.