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| A comparative study of big text data mining methods on tourist emotion computing |
| LI Junyi1,2, REN Tao1,2, LU Luzheng1,2 |
1.School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119,China
2.Shaanxi Key Laboratory of Tourism Informatics, Xi’an 710119, China |
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Abstract The big tourism text data has greatly facilitated the emotional calculation of tourists with its convenience, rapidity and low threshold, and has become a main sources of tourism big data. Under the guidance of big data theory and emotional theory, we adopt the logic / algorithm programming method, machine learning method to mine the big tourism text data, and explore the best tourist emotional computing method. The research findings are as follows: (1) The core of the emotional calculation model for tourists based on sentiment dictionary is to construct emotional dictionary and design emotional calculation rules. The idea is easy to implement with a wide range of applicable corpus.(2)The machine learning method uses statistical methods to extract feature items in the text, and its non-linear feature improves the reliability of emotion calculation comparing with the linear feature of sentiment dictionary method. (3)The emotional calculation of tourists based on the deep learning has a good effect, and the accuracy rate has reached to 85% or higher. The training of multi-domain text corpus is easy to transplant, practical and general, and suitable for the emotional computing research of tourists in the era of big data.
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Received: 10 June 2019
Published: 25 July 2020
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Cite this article:
LI Junyi, REN Tao, LU Luzheng. A comparative study of big text data mining methods on tourist emotion computing. Journal of Zhejiang University (Science Edition), 2020, 47(4): 507-520.
URL:
https://www.zjujournals.com/sci/EN/Y2020/V47/I4/507
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游客情感计算的文本大数据挖掘方法比较研究
旅游文本大数据以其方便、快捷和低门槛的特点为游客情感计算提供了极大便利，已经成为旅游大数据的主要来源之一。基于大数据理论和情感理论，以文本大数据为数据源，在全面梳理国内外情感计算相关成果的基础上，利用人工智能中的逻辑/算法编程方法、机器学习方法、深度学习方法对旅游文本大数据进行挖掘，探索最佳的基于文本大数据的游客情感计算方法。研究发现：（1）基于情感词典的游客情感计算模型，其核心是构建情感词典和设计情感计算规则，方法简单，容易实现，适用语料范围广。（2）机器学习，用统计学方法抽取文本中的特征项，具有非线性特征，可靠性较线性特征的情感词典方法高。（3）基于深度学习技术的游客情感计算，效果良好，准确率在85%以上。训练多领域的文本语料易于移植，实用性强，且泛化能力好，较适合大数据时代游客情感计算研究。
关键词：
深度学习,
旅游文本大数据,
情感计算,
机器学习,
情感词典
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