CrowdTSC：基于群体的神经网络文本情感分类
CrowdTSC: Crowd-based Neural Networks for Text Sentiment Classification
摘要：
情感分类是内容分析中的一项基本任务。尽管与浅层模型相比，深度学习在文本分类方面表现出了良好的性能，但它仍然无法训练令人满意的文本情感分类器。在理解和捕捉文本的情感极性方面，人类比机器学习模型更复杂。在本文中，我们利用人类智能的力量进行文本情感分类。提出了一种基于群体的神经网络用于文本情感分类的方法。我们设计问题并发布在众包平台上，以收集文本中的关键字。抽样和聚类被用来降低众包的成本。此外，我们提出了一个基于注意力的神经网络和一个混合神经网络，将收集的关键字作为人类的指导到深度神经网络。在公共数据集上进行的大量实验证实，CrowdTSC的性能优于最先进的模型，证明了基于人群的关键字指导的有效性。
英文摘要：
Sentiment classification is a fundamental task in content analysis. Although deep learning has demonstrated promising performance in text classification compared with shallow models, it is still not able to train a satisfying classifier for text sentiment. Human beings are more sophisticated than machine learning models in terms of understanding and capturing the emotional polarities of texts. In this paper, we leverage the power of human intelligence into text sentiment classification. We propose Crowd-based neural networks for Text Sentiment Classification (CrowdTSC for short). We design and post the questions on a crowdsourcing platform to collect the keywords in texts. Sampling and clustering are utilized to reduce the cost of crowdsourcing. Also, we present an attention-based neural network and a hybrid neural network, which incorporate the collected keywords as human being's guidance into deep neural networks. Extensive experiments on public datasets confirm that CrowdTSC outperforms state-of-the-art models, justifying the effectiveness of crowd-based keyword guidance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
