NSEmo at EmoInt-2017: An Ensemble to Predict Emotion Intensity in Tweets

Sreekanth Madisetty, Maunendra Sankar Desarkar


Abstract
In this paper, we describe a method to predict emotion intensity in tweets. Our approach is an ensemble of three regression methods. The first method uses content-based features (hashtags, emoticons, elongated words, etc.). The second method considers word n-grams and character n-grams for training. The final method uses lexicons, word embeddings, word n-grams, character n-grams for training the model. An ensemble of these three methods gives better performance than individual methods. We applied our method on WASSA emotion dataset. Achieved results are as follows: average Pearson correlation is 0.706, average Spearman correlation is 0.696, average Pearson correlation for gold scores in range 0.5 to 1 is 0.539, and average Spearman correlation for gold scores in range 0.5 to 1 is 0.514.
Anthology ID:
W17-5230
Volume:
Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
WASSA
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
219–224
Language:
URL:
https://aclanthology.org/W17-5230
DOI:
10.18653/v1/W17-5230
Bibkey:
Cite (ACL):
Sreekanth Madisetty and Maunendra Sankar Desarkar. 2017. NSEmo at EmoInt-2017: An Ensemble to Predict Emotion Intensity in Tweets. In Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 219–224, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
NSEmo at EmoInt-2017: An Ensemble to Predict Emotion Intensity in Tweets (Madisetty & Desarkar, WASSA 2017)
Copy Citation:
PDF:
https://aclanthology.org/W17-5230.pdf