Identifying Tweets that Contain a ’Heartwarming Story’
Manabu Okumura, Yohei Yamaguchi, Masatomo Suzuki, Hiroko Otsuka · 2014
We present a rather new task of detecting and collecting tweets that contain heartwarming stories from a huge amount of tweets on Twitter in this paper. We also present a method for identifying heartwarming tweets. Our prediction method is based on a supervised learning algorithm in SVM along with features from the tweets. We found by comparing the feature sets that adding sentiment features mostly improves the performance. However, simply adding the features for detecting a story in a tweet (past tense and tweet length) cannot contribute to improving the performance, while adding all the features to the baseline feature set mostly yields the best performance from among the feature sets.