Sentiment Lexicon Expansion Based on Neural PU Learning, Double Dictionary Lookup, and Polarity Association

Yasheng Wang, Yang Zhang, Bing Liu


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.
Anthology ID:
D17-1059
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
553–563
Language:
URL:
https://www.aclweb.org/anthology/D17-1059
DOI:
10.18653/v1/D17-1059
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1059.pdf