论文标题
情感分析：预测Yelp得分
Sentiment Analysis: Predicting Yelp Scores
论文作者
论文摘要
在这项工作中，我们预测了基于Yelp Open数据集的子集的餐厅评论的观点。我们利用数据集中可用的元功能和文本，并评估了几种机器学习和预测任务的最新深度学习方法。通过几个定性实验，我们展示了深层模型的成功，并在学习跨不同餐馆的评论的平衡模型中提供了关注机制。最后，我们提出了一种新型的多任务关节BERT模型，以改善整体分类性能。
In this work, we predict the sentiment of restaurant reviews based on a subset of the Yelp Open Dataset. We utilize the meta features and text available in the dataset and evaluate several machine learning and state-of-the-art deep learning approaches for the prediction task. Through several qualitative experiments, we show the success of the deep models with attention mechanism in learning a balanced model for reviews across different restaurants. Finally, we propose a novel Multi-tasked joint BERT model that improves the overall classification performance.
