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【目的】通过对网络游记进行情感分析,发现游客对旅游地情感倾向的时间分布规律。【应用背景】越来越多人通过浏览大量网络游记来收集信息,制定旅游计划。网络游记成为旅游者搭配旅游地及出游时间的重要参考内容,也为商家提供了商机。【方法】提出面向网络游记时间特征的情感分析模型,分析游客情感的时间变化规律。该模型包括5个模块:网络游记文本内容及旅游时间数据的采集、游记文本预处理、情感标注、按时间段统计游记情感特征分值、游记情感时间特征分析。并从网络抓取4种类型旅游地游记对模型进行实验。【结果】在7类情感中,[好]的情感均值在各旅游地的各月份中总是远高于其他情感,较为稳定;[好]、[乐]和[恶]在不同月份的波动程度较大;情感随时间的波动与相应游记数量并不相关,即传统的旅游地旺季和淡季的划分与游客的实际情感体验并不相关。【结论】该模型能够有效地反映旅游地的游客情感随时间变化的波动,进而为旅游管理者、潜在旅游者信息获取提供新的信息参考渠道。
【Objective】 Through the emotional analysis of online travel notes, we find the time distribution of tourists’ affective tendencies to tourist destinations. 【Application Background】 More and more people collect travel information by browsing a large number of online travel notes. Internet travel as a tourist with travel and travel time an important reference, but also for businesses to provide business opportunities. [Methods] The emotion analysis model for the characteristics of online travel time is proposed, and the temporal variation of tourist emotion is analyzed. The model includes five modules: collection of online travel text content and travel time data, travel text preprocessing, emotional annotation, statistical analysis of emotion features of travel notes by time period, and analysis of emotion time characteristics of travel notes. And from the network to crawl four types of travel destination to experiment on the model. [Results] Among the seven kinds of emotions, the emotion mean values of [GOOD] are always much higher than those of other emotions in each month in each tourist destination and are relatively stable. The fluctuations of [GOOD], [LOVE] and [Evil] in different months The degree of emotion is not related to the number of travels. That is to say, the division of the traditional tourist season into peak season and off-season is not related to the actual emotional experience of tourists. 【Conclusion】 The model can effectively reflect the fluctuation of tourists’ emotions over time in tourism destinations, and thus provide new information reference channels for the information acquisition of tourism managers and potential tourists.