Combining At_GRU neural network and emotion dictionary, the emotion classification method of tourism evaluation

Abstract

The invention relates to a tourism evaluation emotion classification method combining At_GRU neural network and emotion dictionary, the present invention is used for realizing the semantic classification of tourism users' evaluation on the whole tourism according to the evaluation text of the whole journey of the tour, and comprises the following steps: 1) processing the emotion characteristics: vectorizing the emotion characteristics in the tourism review by constructing a compound tourism-specific emotion dictionary; 2) data pre-processing stage: training the word vector of the original comment text and splicing the context vector, and fusing the spliced vector and the vectorized emotion feature as the input of the bi-directional GRU neural network; 3) two-way GRU text semantic classification model stage: training two-way GRU neural network and classifying tourism evaluation emotion. Compared with that prior art, the invention has the advantage of high accuracy, considering the accuracy of emotion dictionary, robustness of machine learning and the like.

Description

technical field [0001] The invention relates to the fields of natural language processing and deep learning, in particular to a method for classification of tourism evaluation emotions combined with At_GRU neural network and emotion dictionary. Background technique [0002] Tourist itinerary evaluation is to record tourists’ feedback on a specific itinerary formulated by a certain scenic spot on a travel website. It is the most direct expression of tourists’ satisfaction or suggestions for this itinerary, and is the link between tourists and tourism websites vertically. Through the evaluation of travel routes, passengers can elaborate on the itinerary, accommodation, transportation arrangements, etc. of the route for other passengers to learn from. Travel companies can also listen to opinions directly, quickly respond to improvements, adjust the details of the travel route, improve services, strengthen passenger satisfaction. Therefore, quickly and accurately analyzing and ...

AI Technical Summary

Problems solved by technology

A large number of scholars at home and abroad have done related research. He Youshi proposed multi-feature combination semantic mining based on decision trees. Xia Mingshou and others used ICTCLAS word segmentation technology and word frequency statistics to mine product evaluation features, but failed to use deep learning. Therefore, more accurate features cannot be extracted well. Li Jie and others used the CNN model for short text analysis, but failed to make full use of contextual semantic information, resulting in inaccurate classification problems.

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