@inproceedings{zhou-etal-2021-hot,
title = "Is {\textquotedblleft}hot pizza{\textquotedblright} Positive or Negative? Mining Target-aware Sentiment Lexicons",
author = "Zhou, Jie and
Wu, Yuanbin and
Sun, Changzhi and
He, Liang",
editor = "Merlo, Paola and
Tiedemann, Jorg and
Tsarfaty, Reut",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-main.49/",
doi = "10.18653/v1/2021.eacl-main.49",
pages = "608--618",
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 {\textquotedblleft}big{\textquotedblright} and {\textquotedblleft}long{\textquotedblright}. Given a target (e.g., an aspect), we propose an effective {\textquotedblleft}perturb-and-see{\textquotedblright} 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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T Is “hot pizza” Positive or Negative? Mining Target-aware Sentiment Lexicons
%A Zhou, Jie
%A Wu, Yuanbin
%A Sun, Changzhi
%A He, Liang
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F zhou-etal-2021-hot
%X 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.
%R 10.18653/v1/2021.eacl-main.49
%U https://aclanthology.org/2021.eacl-main.49/
%U https://doi.org/10.18653/v1/2021.eacl-main.49
%P 608-618
Markdown (Informal)
[Is “hot pizza” Positive or Negative? Mining Target-aware Sentiment Lexicons](https://aclanthology.org/2021.eacl-main.49/) (Zhou et al., EACL 2021)
- Is “hot pizza” Positive or Negative? Mining Target-aware Sentiment Lexicons (Zhou et al., EACL 2021)
ACL
- Jie Zhou, Yuanbin Wu, Changzhi Sun, and Liang He. 2021. Is “hot pizza” Positive or Negative? Mining Target-aware Sentiment Lexicons. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 608–618, Online. Association for Computational Linguistics.