  Twitter sentiment analysis, which often focuses on predicting the polarity of
tweets, has attracted increasing attention over the last years, in particular
with the rise of deep learning (DL). In this paper, we propose a new task:
predicting the predominant sentiment among (first-order) replies to a given
tweet. Therefore, we created RETWEET, a large dataset of tweets and replies
manually annotated with sentiment labels. As a strong baseline, we propose a
two-stage DL-based method: first, we create automatically labeled training data
by applying a standard sentiment classifier to tweet replies and aggregating
its predictions for each original tweet; our rationale is that individual
errors made by the classifier are likely to cancel out in the aggregation step.
Second, we use the automatically labeled data for supervised training of a
neural network to predict reply sentiment from the original tweets. The
resulting classifier is evaluated on the new RETWEET dataset, showing promising
results, especially considering that it has been trained without any manually
labeled data. Both the dataset and the baseline implementation are publicly
available.
