@inproceedings{madisetty-desarkar-2017-nsemo,
title = "{NSE}mo at {E}mo{I}nt-2017: An Ensemble to Predict Emotion Intensity in Tweets",
author = "Madisetty, Sreekanth and
Desarkar, Maunendra Sankar",
editor = "Balahur, Alexandra and
Mohammad, Saif M. and
van der Goot, Erik",
booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-5230",
doi = "10.18653/v1/W17-5230",
pages = "219--224",
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.",
}
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%0 Conference Proceedings
%T NSEmo at EmoInt-2017: An Ensemble to Predict Emotion Intensity in Tweets
%A Madisetty, Sreekanth
%A Desarkar, Maunendra Sankar
%Y Balahur, Alexandra
%Y Mohammad, Saif M.
%Y van der Goot, Erik
%S Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F madisetty-desarkar-2017-nsemo
%X 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.
%R 10.18653/v1/W17-5230
%U https://aclanthology.org/W17-5230
%U https://doi.org/10.18653/v1/W17-5230
%P 219-224
Markdown (Informal)
[NSEmo at EmoInt-2017: An Ensemble to Predict Emotion Intensity in Tweets](https://aclanthology.org/W17-5230) (Madisetty & Desarkar, WASSA 2017)
ACL