Aspect-based sentiment analysis with minimal guidance

Honglei Zhuang, Timothy Hanratty, Jiawei Han

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


Aspect-based sentiment analysis is an important tool to understand user opinions in a fine-grained manner. Although extensively studied, developing such a tool for a specific domain remains an expensive process. Most existing methods either rely on massive labeled data for training or external language resource and tools which are not necessarily available or accurate. We propose to study the aspect-based sentiment analysis with only a small set of aspect and sentiment seed words as guidance on a target corpus. We first expand the aspect and sentiment lexicons from the given seed words by features created by frequent pattern mining. Then, we develop a generative model to characterize the aspect and sentiment mentions based on their word embedding, and infer the sentiment polarity for sentiment words accordingly. The effectiveness of our method is verified by experiments on two real world data sets.

Original languageEnglish (US)
Title of host publicationSIAM International Conference on Data Mining, SDM 2019
PublisherSociety for Industrial and Applied Mathematics Publications
Number of pages9
ISBN (Electronic)9781611975673
StatePublished - 2019
Event19th SIAM International Conference on Data Mining, SDM 2019 - Calgary, Canada
Duration: May 2 2019May 4 2019

Publication series

NameSIAM International Conference on Data Mining, SDM 2019


Conference19th SIAM International Conference on Data Mining, SDM 2019

ASJC Scopus subject areas

  • Software


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