@inproceedings{wang-etal-2017-sentiment,
    title = "Sentiment Lexicon Expansion Based on Neural {PU} Learning, Double Dictionary Lookup, and Polarity Association",
    author = "Wang, Yasheng  and
      Zhang, Yang  and
      Liu, Bing",
    editor = "Palmer, Martha  and
      Hwa, Rebecca  and
      Riedel, Sebastian",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/D17-1059/",
    doi = "10.18653/v1/D17-1059",
    pages = "553--563",
    abstract = "Although many sentiment lexicons in different languages exist, most are not comprehensive. In a recent sentiment analysis application, we used a large Chinese sentiment lexicon and found that it missed a large number of sentiment words in social media. This prompted us to make a new attempt to study sentiment lexicon expansion. This paper first poses the problem as a PU learning problem, which is a new formulation. It then proposes a new PU learning method suitable for our problem using a neural network. The results are enhanced further with a new dictionary-based technique and a novel polarity classification technique. Experimental results show that the proposed approach outperforms baseline methods greatly."
}