图 书 馆
使用词嵌入和LSTM进行可操作和政治文本分类Actionable and Political Text Classification Using Word Embeddings and LSTM |
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| 课程网址: | https://videolectures.net/videos/kdd2016_rao_word_embeddings |
| 主讲教师: | Adithya Rao |
| 开课单位: | KDD 2016研讨会 |
| 开课时间: | 2025-02-04 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | 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. |
| 关 键 词: | 词嵌入; LSTM; 文本分类 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2025-03-16：liyq |
| 最后编审: | 2025-03-16：liyq |
| 阅读次数: | 4 |