AAAI Publications, Thirty-First AAAI Conference on Artificial Intelligence

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Distinguish Polarity in Bag-of-Words Visualization
Yusheng Xie, Zhengzhang Chen, Ankit Agrawal, Alok Choudhary

Last modified: 2017-02-12


Neural network-based BOW models reveal that word-embedding vectors encode strong semantic regularities. However, such models are insensitive to word polarity. We show that, coupled with simple information such as word spellings, word-embedding vectors can preserve both semantic regularity and conceptual polarity without supervision. We then describe a nontrivial modification to the t-distributed stochastic neighbor embedding (t-SNE) algorithm that visualizes these semantic- and polarity-preserving vectors in reduced dimensions. On a real Facebook corpus, our experiments show significant improvement in t-SNE visualization as a result of the proposed modification.


t-SNE;sentiment;word embedding; auto encoder

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