This paper reports our submissions to Task 4, i.e., Sentiment Analysis in Twitter (SAT), in SemEval 2016, which consists of five subtasks grouped into two levels: (1) sentence level, i.e., message polarity classification (subtask A), and (2) topic level, i.e., tweet classification and quantification according to two-point scale (subtask B and D) or five-point scale (subtask C and E). We participated in all these five subtasks. To address these subtasks, we investigated several traditional Natural Language Processing (NLP) features including sentiment lexicon, linguistic and domain specific features, and word embedding features together with supervised machine learning methods. Officially released results showed that our systems rank above average.
['Yunxiao Zhou', 'Zhihua Zhang', 'Man Lan']
ECNU at SemEval-2016 Task 4: An Empirical Investigation of Traditional NLP Features and Word Embedding Features for Sentence-level and Topic-level Sentiment Analysis in Twitter