Learning Word Representations for Sentiment Analysis

Yang Li, Quan Pan, Tao Yang, Suhang Wang, Jiliang Tang, Erik Cambria

Research output: Contribution to journalArticlepeer-review

138 Scopus citations


Word embedding has been proven to be a useful model for various natural language processing tasks. Traditional word embedding methods merely take into account word distributions independently from any specific tasks. Hence, the resulting representations could be sub-optimal for a given task. In the context of sentiment analysis, there are various types of prior knowledge available, e.g., sentiment labels of documents from available datasets or polarity values of words from sentiment lexicons. We incorporate such prior sentiment information at both word level and document level in order to investigate the influence each word has on the sentiment label of both target word and context words. By evaluating the performance of sentiment analysis in each category, we find the best way of incorporating prior sentiment information. Experimental results on real-world datasets demonstrate that the word representations learnt by DLJT2 can significantly improve the sentiment analysis performance. We prove that incorporating prior sentiment knowledge into the embedding process has the potential to learn better representations for sentiment analysis.

Original languageEnglish (US)
Pages (from-to)843-851
Number of pages9
JournalCognitive Computation
Issue number6
StatePublished - Dec 1 2017

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Cognitive Neuroscience


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