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Issue No. 02 - Feb. (2016 vol. 28)
ISSN: 1041-4347
pp: 496-509
Duyu Tang , , Harbin Institute of Technology, Harbin, China
Furu Wei , , Microsoft Research, Beijing, China
Bing Qin , , Harbin Institute of Technology, Harbin, China
Nan Yang , , Microsoft Research, Beijing, China
Ting Liu , , Harbin Institute of Technology, Harbin, China
Ming Zhou , , Microsoft Research, Beijing, China
We propose learning sentiment-specific word embeddings dubbed sentiment embeddings in this paper. Existing word embedding learning algorithms typically only use the contexts of words but ignore the sentiment of texts. It is problematic for sentiment analysis because the words with similar contexts but opposite sentiment polarity, such as good and bad, are mapped to neighboring word vectors. We address this issue by encoding sentiment information of texts (e.g., sentences and words) together with contexts of words in sentiment embeddings. By combining context and sentiment level evidences, the nearest neighbors in sentiment embedding space are semantically similar and it favors words with the same sentiment polarity. In order to learn sentiment embeddings effectively, we develop a number of neural networks with tailoring loss functions, and collect massive texts automatically with sentiment signals like emoticons as the training data. Sentiment embeddings can be naturally used as word features for a variety of sentiment analysis tasks without feature engineering. We apply sentiment embeddings to word-level sentiment analysis, sentence level sentiment classification, and building sentiment lexicons. Experimental results show that sentiment embeddings consistently outperform context-based embeddings on several benchmark datasets of these tasks. This work provides insights on the design of neural networks for learning task-specific word embeddings in other natural language processing tasks.
Context, Context modeling, Predictive models, Neural networks, Mathematical model, Sentiment analysis, Vocabulary,Neural Networks, Natural Language Processing, Word Embeddings, Sentiment Analysis,neural networks, Natural language processing, word embeddings, sentiment analysis
Duyu Tang, Furu Wei, Bing Qin, Nan Yang, Ting Liu, Ming Zhou, "Sentiment Embeddings with Applications to Sentiment Analysis", IEEE Transactions on Knowledge & Data Engineering, vol. 28, no. , pp. 496-509, Feb. 2016, doi:10.1109/TKDE.2015.2489653
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