NEUDM: A System for Topic-Based Message Polarity Classification

In this paper, we describe our system for the topic-based Chinese message polarity classification in SIGHAN 8 Task 2. Our system integrates two SVM classifiers which consist of LinearSVC and LibSVM to train the classification model and predict the results of Chinese message polarity in the restricted resource and the unrestricted resource, respectively. In order to assure our feature engineering effort on the task, we use some feature selection methods, such as LDA, word2vec, and sentiment lexicons including DLUT emotion ontology and NTUSD. Our system achieves the overall F1 score of 74.88% in the restricted evaluation and 74.43% in the unrestricted evaluation.