Joint Aspect-Sentiment Analysis with Minimal User Guidance

Honglei Zhuang, Fang Guo, Chao Zhang, Liyuan Liu, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Aspect-based sentiment analysis is a substantial step towards text understanding which benefits numerous applications. Since most existing algorithms require a large amount of labeled data or substantial external language resources, applying them on a new domain or a new language is usually expensive and time-consuming. We aim to build an aspect-based sentiment analysis model from an unlabeled corpus with minimal guidance from users, i.e., only a small set of seed words for each aspect class and each sentiment class. We employ an autoencoder structure with attention to learn two dictionary matrices for aspect and sentiment respectively where each row of the dictionary serves as an embedding vector for an aspect or a sentiment class. We propose to utilize the user-given seed words to regularize the dictionary learning. In addition, we improve the model by joining the aspect and sentiment encoder in the reconstruction of sentiment in sentences. The joint structure enables sentiment embeddings in the dictionary to be tuned towards the aspect-specific sentiment words for each aspect, which benefits the classification performance. We conduct experiments on two real data sets to verify the effectiveness of our models.

Original languageEnglish (US)
Title of host publicationSIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages1241-1250
Number of pages10
ISBN (Electronic)9781450380164
DOIs
StatePublished - Jul 25 2020
Event43rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2020 - Virtual, Online, China
Duration: Jul 25 2020Jul 30 2020

Publication series

NameSIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

Conference

Conference43rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2020
CountryChina
CityVirtual, Online
Period7/25/207/30/20

Keywords

  • aspect-based sentiment analysis
  • autoencoder
  • weakly-supervised

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Information Systems
  • Software

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