方面情感四元组抽取用于推荐领域研究
青海大学, 计算机技术与应用学院, 青海西宁 810016
| 摘 要： | 为了增强推荐系统的准确性、新颖性和多样性，研究人员引入了用户对项目的评论去增强推荐。但是现有的情感分析数据集并不适配推荐领域，使用情感领域的预训练模型去抽取推荐领域的评论情感效果往往不佳，而现有的方面情感推荐的研究大多忽略了这个问题。在这项工作中，将方面情感四元组抽取引入推荐中，其目标是尽可能多的提取评论文本中的Aspect-Category-Opinion-Sentiment情感四元组。为此，进一步构建了三个新的数据集Amazon-book、Movies和Yelp。Amazon-book是亚马逊图书推荐情感数据集；Movies是基于MovieLens推荐领域的情感数据集，Yelp是Yelp网站的情感数据集。这三个数据集不仅包含Aspect-Category-Opinion-Sentiment四元组的注释，而且还包含隐含的Aspect和Opinion。最后使用四个四元组方面情感数据集与所构建的三个推荐领域情感数据集进行对比，实验结果证明了三个推荐领域情感数据集的可用性以及应用推荐领域的可行性。 |
| 关 键 词： | 方面情感; 标签数据集; 四元组情感抽取; 情感推荐 |
| DOI： | 10.57237/j.cst.2024.04.001 |
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收稿日期: 2024-09-06
接受日期: 2024-11-04
在线出版日期: 2024-12-05
Aspect Sentiment Quadruple Extraction for Recommendation Research
School of Computer Technology and Applications, Qinghai University, Xining 810016, China
| Abstract: | To enhance the accuracy, novelty and diversity of the recommendation system, researchers introduced user comments on items to enhance the recommendation. However, the existing sentiment analysis datasets are not suitable for the recommendation field. The effect of using the pre-trained model in the sentiment field to extract the comment sentiment in the recommendation field is often poor, and most of the existing research on aspect sentiment recommendation ignores this problem. In this work, aspect sentiment quadruple extraction is introduced into the recommendation, and its goal is to extract all aspect-category-opinion-sentiment quadruple in the comment text. To this end, three new datasets Amazon-book, Movies and Yelp are further constructed. Amazon-book is an Amazon book recommendation sentiment dataset; Movies is a sentiment dataset based on the MovieLens recommendation field, and Yelp is a sentiment dataset for the yelp website. These three datasets not only contain annotations of the Aspect-Category-Opinion-Sentiment quadruple, but also contain implicit Aspect and Opinion. Finally, the datasets are tested using four quadruple aspect sentiment models, and the experiments prove the availability of the three recommendation field sentiment datasets and the feasibility of applying them to the recommendation field. |
| Keywords: | Aspect Sentiment; Label Dataset; Quadruple Sentiment Extraction; Sentiment Recommendation |
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