Exploring Optimism and Pessimism in Twitter Using Deep Learning

Cornelia Caragea, Liviu P. Dinu, Bogdan Dumitru


Abstract
Identifying optimistic and pessimistic viewpoints and users from Twitter is useful for providing better social support to those who need such support, and for minimizing the negative influence among users and maximizing the spread of positive attitudes and ideas. In this paper, we explore a range of deep learning models to predict optimism and pessimism in Twitter at both tweet and user level and show that these models substantially outperform traditional machine learning classifiers used in prior work. In addition, we show evidence that a sentiment classifier would not be sufficient for accurately predicting optimism and pessimism in Twitter. Last, we study the verb tense usage as well as the presence of polarity words in optimistic and pessimistic tweets.
Anthology ID:
D18-1067
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
652–658
Language:
URL:
https://aclanthology.org/D18-1067
DOI:
10.18653/v1/D18-1067
Bibkey:
Cite (ACL):
Cornelia Caragea, Liviu P. Dinu, and Bogdan Dumitru. 2018. Exploring Optimism and Pessimism in Twitter Using Deep Learning. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 652–658, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Exploring Optimism and Pessimism in Twitter Using Deep Learning (Caragea et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1067.pdf
Video:
 https://aclanthology.org/D18-1067.mp4
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