Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2019

Abstract

Various types of target information have been considered in aspect-based sentiment analysis, such as entities and aspects. Existing research has realized the importance of targets and developed methods with the goal of precisely modeling their contexts via generating target-specific representations. However, all these methods ignore that these representations cannot be learned well due to the lack of sufficient human-annotated target-related reviews, which leads to the data sparsity challenge, a.k.a. cold-start problem here. In this paper, we focus on a more general multiple entity aspect-based sentiment analysis (ME-ABSA) task which aims at identifying the sentiment polarity of different aspects of multiple entities in their context. Faced with severe cold-start scenario, we develop a novel and extensible deep memory network framework with cold-start aware computational layers which use frequency-guided attention mechanism to accentuate on the most related targets, and then compose their representations into a complementary vector for enhancing the representations of cold-start entities and aspects. To verify the effectiveness of the framework, we instantiate it with a concrete context encoding method and then apply the model to the ME-ABSA task. Experimental results conducted on two public datasets demonstrate that the proposed approach outperforms state-of-the-art baselines on ME-ABSA task.

Keywords

Natural Language Processing, Sentiment Analysis and Text Mining, Natural Language Processing, Text Classification

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019)

First Page

5197

Last Page

5203

Identifier

10.24963/ijcai.2019/722

Publisher

AAAI Press

City or Country

Macau, China

Additional URL

https://doi.org/10.24963/ijcai.2019/722

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