Emotional Calculation Method of Rural Tourist Based on Improved SPCA-LSTM Algorithm

Abstract

New technologies such as big data and cloud computing provide new means and tools for rural development. The big rural tourism, with its convenience, quickness, and low threshold, presents great convenience for tourists’ emotional calculation and has become one of the main sources of tourism big data. Under the guidance of big data theory and emotion theory, this paper proposes an emotional calculation method of rural tourists based on improved SPCA-LSTM algorithm, taking big text data as data source. Firstly, the improved TF-IDF algorithm is designed to highlight the importance of feature items, and the word vector trained by word2vec model is applied to represent the rural tourism data text. Then, a weighted sparse PCA (RSPCA) is constructed to reduce the dimension of massive word vector features. RSPCA introduces the weighted $l_1$ optimization framework and LASSO regression model into the mathematical model of PCA algorithm and establishes a new data dimension reduction model. Thereupon, the long-term and short-term memory convolution network with attention mechanism is employed to extract text features. Finally, the feature vector is utilized to calculate the rural tourist’s emotion by softmax function. The experimental results indicate that the improved SPCA-LSTM algorithm, whose performance index is better than other existing algorithms, is effective in calculating tourists’ emotions. Also, it is more suitable for the research of tourist sentiment calculation in the era of big data.