Automatic Aggregation by Joint Modeling of Aspects and Values
Automatic Aggregation by Joint Modeling of Aspects and Values
复制标题
通过方面和值的联合建模自动聚合
DOI：
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发表时间：
2014
影响因子：
5
通讯作者：
R. Barzilay
中科院分区：
文献类型：
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作者：
Christina Sauper;R. Barzilay
提出了一种基于联合方面识别和情感分析的产品评论片段聚合模型。我们的模型同时识别出产品评论（例如，日本餐厅的寿司和味噌）中呈现的一组潜在的可评价方面，并确定每个方面的相应情绪。这种方法可以直接发现产品的高评价或不一致的方面。我们的生成模型允许一种有效的变分平均场推理算法。它也很容易扩展，我们描述了一些修改及其对模型结构和推理的影响。我们在两个任务上测试了我们的模型，在一组Yelp评论上进行联合方面识别和情感分析，在一组医学摘要上单独进行方面识别。我们评估了模型在方面识别、情感分析和每个单词标注准确性方面的性能。我们证明，我们的模型在相当大的范围内优于适用的基线，在方面识别上产生高达32%的相对误差减少，在情感分析上产生高达20%的相对误差减少。
We present a model for aggregation of product review snippets by joint aspect identification and sentiment analysis. Our model simultaneously identifies an underlying set of ratable aspects presented in the reviews of a product (e.g., sushi and miso for a Japanese restaurant) and determines the corresponding sentiment of each aspect. This approach directly enables discovery of highly-rated or inconsistent aspects of a product. Our generative model admits an efficient variational mean-field inference algorithm. It is also easily extensible, and we describe several modifications and their effects on model structure and inference. We test our model on two tasks, joint aspect identification and sentiment analysis on a set of Yelp reviews and aspect identification alone on a set of medical summaries. We evaluate the performance of the model on aspect identification, sentiment analysis, and per-word labeling accuracy. We demonstrate that our model outperforms applicable baselines by a considerable margin, yielding up to 32% relative error reduction on aspect identification and up to 20% relative error reduction on sentiment analysis.