Sentiment Classification with Convolutional Neural Network Using Multiple Word Representations

Most neural network models for sentiment classification use word vectors pre-trained by word embedding methods to represent a word. Although word vectors are trained on large corpus, most of them are restricted by the vocabularies in the corpus. Since sentiment classification models have to capture subtle meaning of sentence, it is desirable to represent words that have not been pre-trained by word embedding method. To achieve this goal, we propose a sentiment classification model with convolutional neural network using multiple word representations. We represent a word by three embedding methods including word2vec, GloVe, and our method which is based on a character level embedding method that successfully captures subtle differences between words. Experimental results from three datasets show that our model with an additional character level embedding method improves the accuracy of the sentiment classification.