Transfer Learning for Emotional Polarity Classification
Transfer Learning for Emotional Polarity Classification
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DOI：
10.1109/wi-iat.2014.85
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发表时间：
2014-08
期刊：
影响因子：
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通讯作者：
Q. Vuong;A. Takasu
中科院分区：
文献类型：
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作者：
Q. Vuong;A. Takasu
情感极性分类是情感分析领域的一项重要任务。它被应用于许多实际问题，如消费者产品和服务的审查、金融市场和法医分析。来自文本挖掘和自然语言处理领域的科学家们研究了如何解决情感极性分类问题。他们使用了各种方法，从简单的方法(例如，基于词典的分类)到复杂的方法(例如，统计模型)。然而，在分布不同于训练集的新测试集中，统计模型的问题并不适用。因此，情绪极性分类问题的准确性仍然不稳定。在本文中，我们提出了一种新的方法形式主义来解决这个问题，使用自适应迁移学习。转移学习利用已标记的数据来解决相关但不同的问题。我们还提出了一种新的方法，使用这种方法来提高性能。在两个合成数据集和三个真实Twitter数据集上的实验结果验证了该方法的有效性。
Emotional Polarity Classification is an important task in Sentiment Analysis area. It is applied in many real problems such as reviews of consumer products and services, financial markets, and forensic analysis. The scientists from the areas of text mining and nature language processing have studied how to solve emotional polarity classification problem. They used a variety of methods, from simple methods (e.g., Lexicon-based categorization) to sophisticate methods (e.g., Statistical models). However, the problem of statistical models does not work well in a new test set whose distribution is different from training set. Therefore, the accuracy of Emotional Polarity Classification problem is still unstable. In this paper, we propose a novel approach formalism to solve this problem by using adaptation transfer learning. The transfer learning utilizes the labelled data available to solve the related but different problems. We also propose a new method that uses this approach to improve performance. The effectiveness of our approach is verified by the experiment results with two synthesis datasets and three real Twitter datasets.