Text-based geolocation prediction of social media users with neural networks
Text-based geolocation prediction of social media users with neural networks
复制标题
使用神经网络对社交媒体用户进行基于文本的地理位置预测
DOI：
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发表时间：
2017
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通讯作者：
ChengXiang Zhai
中科院分区：
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作者：
Ismini Lourentzou;Alex Morales;ChengXiang Zhai
推断用户的位置对于许多利用社交媒体的应用程序来说是有价值的一步，例如营销、安全监控和推荐系统。最近，深度学习技术在计算机视觉、语音识别和自然语言处理等许多其他任务中取得了成功，受此启发，我们研究了神经网络在地理位置预测问题中的应用，并尝试了多种技术来改进神经网络，用于仅基于文本的地理位置推理。在三个Twitter数据集上的实验结果表明，选择合适的网络结构、激活函数和执行批量归一化都可以提高该任务的性能。
Inferring the location of a user has been a valuable step for many applications that leverage social media, such as marketing, security monitoring and recommendation systems. Motivated by the recent success of Deep Learning techniques for many other tasks such as computer vision, speech recognition, and natural language processing, we study the application of neural networks to the problem of geolocation prediction and experiment with multiple techniques to improve neural networks for geolocation inference based solely on text. Experimental results on three Twitter datasets suggest that choosing appropriate network architecture, activation function, and performing Batch Normalization, can all increase performance on this task.