Affiliation:
1. National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, China
Abstract
Fine-grained sentiment analysis is a useful tool for producers to understand consumers’ needs as well as complaints about products and related aspects from online platforms. In this article, we define a novel task named “Multi-Entity Aspect-Based Sentiment Analysis (ME-ABSA)”. It investigates the sentiment towards entities and their related aspects. It makes the well-studied aspect-based sentiment analysis a special case of this type, where the number of entities is limited to one. We contribute a new dataset for this task, with multi-entity Chinese posts in it. We propose to model context, entity, and aspect memory to address the task and incorporate dependency information for further improvement. Experiments show that our methods perform significantly better than baseline methods on datasets for both ME-ABSA task and ABSA task. The in-depth analysis further validates the effectiveness of our methods and shows that our methods are capable of generalizing to new (entity, aspect) combinations with little loss of accuracy. This observation indicates that data annotation in real applications can be largely simplified.
Funder
the National Key Research and Development Program of China
the National Natural Science Foundation of China
the Fundamental Research Funds for the Central Universities
Publisher
Association for Computing Machinery (ACM)
Reference54 articles.
1. Improved Neural Machine Translation with a Syntax-Aware Encoder and Decoder
2. Neural Machine Translation with Source Dependency Representation
3. Kehai Chen Tiejun Zhao Muyun Yang and Lemao Liu. 2017. Translation prediction with source dependency-based context representation.. In AAAI. 3166--3172. Kehai Chen Tiejun Zhao Muyun Yang and Lemao Liu. 2017. Translation prediction with source dependency-based context representation.. In AAAI. 3166--3172.
4. Joint Prediction for Entity/Event-Level Sentiment Analysis using Probabilistic Soft Logic Models
Cited by
18 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献