Learning Sentiment Composition from Sentiment Lexicons

Orith Toledo-Ronen, Roy Bar-Haim, Alon Halfon, Charles Jochim, Amir Menczel, Ranit Aharonov, Noam Slonim


Abstract
Sentiment composition is a fundamental sentiment analysis problem. Previous work relied on manual rules and manually-created lexical resources such as negator lists, or learned a composition function from sentiment-annotated phrases or sentences. We propose a new approach for learning sentiment composition from a large, unlabeled corpus, which only requires a word-level sentiment lexicon for supervision. We automatically generate large sentiment lexicons of bigrams and unigrams, from which we induce a set of lexicons for a variety of sentiment composition processes. The effectiveness of our approach is confirmed through manual annotation, as well as sentiment classification experiments with both phrase-level and sentence-level benchmarks.
Anthology ID:
C18-1189
Volume:
Proceedings of the 27th International Conference on Computational Linguistics
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Editors:
Emily M. Bender, Leon Derczynski, Pierre Isabelle
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2230–2241
Language:
URL:
https://aclanthology.org/C18-1189
DOI:
Bibkey:
Cite (ACL):
Orith Toledo-Ronen, Roy Bar-Haim, Alon Halfon, Charles Jochim, Amir Menczel, Ranit Aharonov, and Noam Slonim. 2018. Learning Sentiment Composition from Sentiment Lexicons. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2230–2241, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):
Learning Sentiment Composition from Sentiment Lexicons (Toledo-Ronen et al., COLING 2018)
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PDF:
https://aclanthology.org/C18-1189.pdf