3 research outputs found
Recurrent Entity Networks with Delayed Memory Update for Targeted Aspect-based Sentiment Analysis
While neural networks have been shown to achieve impressive results for
sentence-level sentiment analysis, targeted aspect-based sentiment analysis
(TABSA) --- extraction of fine-grained opinion polarity w.r.t. a pre-defined
set of aspects --- remains a difficult task. Motivated by recent advances in
memory-augmented models for machine reading, we propose a novel architecture,
utilising external "memory chains" with a delayed memory update mechanism to
track entities. On a TABSA task, the proposed model demonstrates substantial
improvements over state-of-the-art approaches, including those using external
knowledge bases.Comment: Accepted to NAACL 2018 (camera-ready
Targeted Sentiment Analysis: A Data-Driven Categorization
Targeted sentiment analysis (TSA), also known as aspect based sentiment
analysis (ABSA), aims at detecting fine-grained sentiment polarity towards
targets in a given opinion document. Due to the lack of labeled datasets and
effective technology, TSA had been intractable for many years. The newly
released datasets and the rapid development of deep learning technologies are
key enablers for the recent significant progress made in this area. However,
the TSA tasks have been defined in various ways with different understandings
towards basic concepts like `target' and `aspect'. In this paper, we categorize
the different tasks and highlight the differences in the available datasets and
their specific tasks. We then further discuss the challenges related to data
collection and data annotation which are overlooked in many previous studies.Comment: Draf
Country Image in COVID-19 Pandemic: A Case Study of China
Country image has a profound influence on international relations and
economic development. In the worldwide outbreak of COVID-19, countries and
their people display different reactions, resulting in diverse perceived images
among foreign public. Therefore, in this study, we take China as a specific and
typical case and investigate its image with aspect-based sentiment analysis on
a large-scale Twitter dataset. To our knowledge, this is the first study to
explore country image in such a fine-grained way. To perform the analysis, we
first build a manually-labeled Twitter dataset with aspect-level sentiment
annotations. Afterward, we conduct the aspect-based sentiment analysis with
BERT to explore the image of China. We discover an overall sentiment change
from non-negative to negative in the general public, and explain it with the
increasing mentions of negative ideology-related aspects and decreasing
mentions of non-negative fact-based aspects. Further investigations into
different groups of Twitter users, including U.S. Congress members, English
media, and social bots, reveal different patterns in their attitudes toward
China. This study provides a deeper understanding of the changing image of
China in COVID-19 pandemic. Our research also demonstrates how aspect-based
sentiment analysis can be applied in social science researches to deliver
valuable insights