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    <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.</abstract>
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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)

• NSEmo at EmoInt-2017: An Ensemble to Predict Emotion Intensity in Tweets (Madisetty & Desarkar, WASSA 2017)

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.