YZU-NLP team at SemEval-2016 Task 4: Ordinal Sentiment classification using a recurrent convolutional network

Abstract

Sentiment analysis of tweets has attracted considerable attention recently for potential use in commercial and public sector applications. Typical sentiment analysis classifies the sentiment of sentences into several discrete classes (e.g., positive and negative). The aim of Task 4 subtask C of SemEval-2016 is to classify the sentiment of tweets into an ordinal five-point scale. In this paper, we present a system that uses word embeddings and recurrent convolutional networks to complete the competition task. The word embeddings provide a continuous vector representation of words for the recurrent convolutional network to use in building sentence vectors for multipoint classification. The proposed method ranked second among eleven teams in terms of micro-averaged MAE (mean absolute error) and eighth for macro-averaged MAE.