RoseMerry: A Baseline Message-level Sentiment Classification System
Huizhi Liang, Richard Fothergill, Timothy J. Baldwin · 2015
In this paper, we propose a baseline messagelevel sentiment classification method, as developed for SemEval-2015 Task 10, Subtask B. This system leverages both hand-crafted features and message-level embedding features, and uses an SVM classifier for messagelevel sentiment classification.In pre-training the embedding features, we use one million randomly-selected tweets.We present results over SemEval-2015 Task 10, Subtask B, as well as the Stanford Sentiment Treebank.Our experiments show the effectiveness of our method over both datasets.