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 |
| DOI: | 10.57237/j.cst.2024.04.001 |
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Received: 6 September 2024
Accepted: 4 November 2024
Published Online: 5 December 2024
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