NSEmo at EmoInt-2017: An Ensemble to Predict Emotion Intensity in Tweets
Sreekanth Madisetty, Maunendra Sankar Desarkar · 2017
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 contentbased features (hashtags, emoticons, elongated words, etc.).The second method considers word n-grams and character ngrams for training.The final method uses lexicons, word embeddings, word ngrams, 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.