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

Madisetty, Sreekanth and Desarkar, Maunendra Sankar (2017) NSEmo at EmoInt-2017: An Ensemble to Predict Emotion Intensity in Tweets. In: 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, 7–11, September 2017, Copenhagen, Denmark.

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Abstract

In this paper, we describe a method to pre- dict emotion intensity in tweets. Our ap- proach is an ensemble of three regression methods. The first method uses content- based features (hashtags, emoticons, elon- gated 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 meth- ods gives better performance than individ- ual methods. We applied our method on WASSA emotion dataset. Achieved re- sults are as follows: average Pearson cor- relation is 0.706, average Spearman cor- relation is 0.696, average Pearson corre- lation 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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IITH Creators:
IITH CreatorsORCiD
Desarkar, Maunendra SankarUNSPECIFIED
Item Type: Conference or Workshop Item (Paper)
Subjects: Computer science
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 12 Sep 2017 05:04
Last Modified: 12 Sep 2017 05:04
URI: http://raiith.iith.ac.in/id/eprint/3538
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