Is “hot pizza” Positive or Negative? Mining Target-aware Sentiment Lexicons

Jie Zhou, Yuanbin Wu, Changzhi Sun, Liang He


Abstract
Modelling a word’s polarity in different contexts is a key task in sentiment analysis. Previous works mainly focus on domain dependencies, and assume words’ sentiments are invariant within a specific domain. In this paper, we relax this assumption by binding a word’s sentiment to its collocation words instead of domain labels. This finer view of sentiment contexts is particularly useful for identifying commonsense sentiments expressed in neural words such as “big” and “long”. Given a target (e.g., an aspect), we propose an effective “perturb-and-see” method to extract sentiment words modifying it from large-scale datasets. The reliability of the obtained target-aware sentiment lexicons is extensively evaluated both manually and automatically. We also show that a simple application of the lexicon is able to achieve highly competitive performances on the unsupervised opinion relation extraction task.
Anthology ID:
2021.eacl-main.49
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Month:
April
Year:
2021
Address:
Online
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
608–618
Language:
URL:
https://aclanthology.org/2021.eacl-main.49
DOI:
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.eacl-main.49.pdf