Sentiment Analysis and Lexical Cohesion for the Story Cloze Task

Michael Flor, Swapna Somasundaran


Abstract
We present two NLP components for the Story Cloze Task – dictionary-based sentiment analysis and lexical cohesion. While previous research found no contribution from sentiment analysis to the accuracy on this task, we demonstrate that sentiment is an important aspect. We describe a new approach, using a rule that estimates sentiment congruence in a story. Our sentiment-based system achieves strong results on this task. Our lexical cohesion system achieves accuracy comparable to previously published baseline results. A combination of the two systems achieves better accuracy than published baselines. We argue that sentiment analysis should be considered an integral part of narrative comprehension.
Anthology ID:
W17-0909
Volume:
Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics
Month:
April
Year:
2017
Address:
Valencia, Spain
Venues:
LSDSem | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
62–67
Language:
URL:
https://aclanthology.org/W17-0909
DOI:
10.18653/v1/W17-0909
Bibkey:
Cite (ACL):
Michael Flor and Swapna Somasundaran. 2017. Sentiment Analysis and Lexical Cohesion for the Story Cloze Task. In Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics, pages 62–67, Valencia, Spain. Association for Computational Linguistics.
Cite (Informal):
Sentiment Analysis and Lexical Cohesion for the Story Cloze Task (Flor & Somasundaran, 2017)
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PDF:
https://aclanthology.org/W17-0909.pdf