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

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.