基于词嵌入和LSTM的可操作和政治文本分类
Actionable and Political Text Classification using Word Embeddings and LSTM
摘要：
在这项工作中，我们将词嵌入和具有长短期记忆（LSTM）的神经网络应用于文本分类问题，其中分类标准由应用程序的上下文决定。我们特别研究两个应用程序。第一个是可操作性，我们构建模型将来自服务提供商客户的社交媒体消息分类为可操作或不可操作。我们为超过30种不同的语言构建了可操作性模型，大多数模型的准确率达到85%左右，有些模型的准确率超过90%。我们还表明，使用带有词嵌入的LSTM神经网络大大优于传统技术。其次，我们探讨了信息的分类与政治倾向，其中社交媒体信息被归类为民主党或共和党。该模型能够以87.57%的高准确率对消息进行分类。作为实验的一部分，我们改变了神经网络的不同超参数，并报告了这种变化对准确性的影响。这些可操作性模型已经部署到生产环境中，并帮助公司代理通过优先考虑要响应的消息来提供客户支持。政治学习的模式已经开放，并可供更广泛地使用。
英文摘要：
In this work, we apply word embeddings and neural networks with Long Short-Term Memory (LSTM) to text classification problems, where the classification criteria are decided by the context of the application. We examine two applications in particular. The first is that of Actionability, where we build models to classify social media messages from customers of service providers as Actionable or Non-Actionable. We build models for over 30 different languages for actionability, and most of the models achieve accuracy around 85%, with some reaching over 90% accuracy. We also show that using LSTM neural networks with word embeddings vastly outperform traditional techniques. Second, we explore classification of messages with respect to political leaning, where social media messages are classified as Democratic or Republican. The model is able to classify messages with a high accuracy of 87.57%. As part of our experiments, we vary different hyperparameters of the neural networks, and report the effect of such variation on the accuracy. These actionability models have been deployed to production and help company agents provide customer support by prioritizing which messages to respond to. The model for political leaning has been opened and made available for wider use.
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。
