Affiliations: [a] College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, China
| [b] College of Computer Science and Engineering, Shandong Province Key Laboratory of Wisdom Mine Information Technology, Shandong University of Science and Technology, Qingdao, China
Correspondence:
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Corresponding authors. Zhongying Zhao, College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China. E-mail: [email protected]. and Chao Li, College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, China, E-mail: [email protected].
Abstract: Word embeddings have been successfully applied in many natural language processing tasks due to its their effectiveness. However, the state-of-the-art algorithms for learning word representations from large amounts of text documents ignore emotional information, which is a significant research problem that must be addressed. To solve the above problem, we propose an emotional word embedding (EWE) model for sentiment analysis in this paper. This method first applies pre-trained word vectors to represent document features using two different linear weighting methods. Then, the resulting document vectors are input to a classification model and used to train a text sentiment classifier, which is based on a neural network. In this way, the emotional polarity of the text is propagated into the word vectors. The experimental results on three kinds of real-world data sets demonstrate that the proposed EWE model achieves superior performances on text sentiment prediction, text similarity calculation, and word emotional expression tasks compared to other state-of-the-art models.
Keywords: Sentiment analysis, word embedding, classification, representation learning
