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Domain-Aware Sentiment Classification
with GRUs and CNNs
(1st place in the Semantic Sentiment Classification Challenge at ESWC2018)
Guangyuan Piao, John G. Breslin
Insight Centre for Data Analytics @NUI Galway
03/06/2018	–	07/06/2018	Semantic	Sentiment	Analysis	&	Embedding	Challenge	@ESWC2018
2
Tasks
2
embedding evaluation
●  9 provided embeddings
●  2 baseline embeddings (glove, amazonwe)
sentiment classification
●  1 million reviews
●  20 domains
●  50% positive & 50% negative
our design dimension
●  domain-aware
●  combining RNNs & CNNs
●  runnable under our limited resources
3
3
....
x1 x2 xm....
review text
dr
review 
domain
....
GRU
CNN
....
look up
S1 S2 Sn....
dense
y'
review summary
prediction
dr
....
GRU
CNN
concatenation layer
Proposed Model
4
Results on Validation Set
4
training: the rest of provided dataset / validation: 10,000 of 20 domains
ensemble approach: voting with 11 models using different emb. & epochs
embedding (emb.) accuracy
glove 0.9525
amazonwe 0.9122
emb_128_15 0.9578
emb_128_30 0.9560
emb_128_50 0.9544
emb_256_15 0.9576
emb_256_30 0.9576
emb_256_50 0.9578
emb_512_15 0.9568
emb_512_30 0.9598
emb_512_50 0.9605
ensemble approach 0.9657
5
Results on Test Set
5
test: 10,000 equally distributed over 20 domains
team accuracy
our approach 0.9643
Team#2 0.9561
Team#3 0.9356
Team#4 0.9228
Team#5 0.8823
Team#6 0.8743
… …
… …
funded by
thank you for your attention!
Guangyuan Piao, John G. Breslin, Domain-Aware Sentiment
Classification with GRUs and CNNs, Semantic Sentiment
Classification Challenge at ESWC2018, Crete, Greece
Guangyuan Piao
homepage: http://parklize.github.io
e-mail: guangyuan.piao@insight-centre.org
scholar: https://goo.gl/tgK9bk
slideshare: http://www.slideshare.net/parklize

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Domain-Aware Sentiment Classification with GRUs and CNNs

  • 1. Domain-Aware Sentiment Classification with GRUs and CNNs (1st place in the Semantic Sentiment Classification Challenge at ESWC2018) Guangyuan Piao, John G. Breslin Insight Centre for Data Analytics @NUI Galway 03/06/2018 – 07/06/2018 Semantic Sentiment Analysis & Embedding Challenge @ESWC2018
  • 2. 2 Tasks 2 embedding evaluation ●  9 provided embeddings ●  2 baseline embeddings (glove, amazonwe) sentiment classification ●  1 million reviews ●  20 domains ●  50% positive & 50% negative our design dimension ●  domain-aware ●  combining RNNs & CNNs ●  runnable under our limited resources
  • 3. 3 3 .... x1 x2 xm.... review text dr review  domain .... GRU CNN .... look up S1 S2 Sn.... dense y' review summary prediction dr .... GRU CNN concatenation layer Proposed Model
  • 4. 4 Results on Validation Set 4 training: the rest of provided dataset / validation: 10,000 of 20 domains ensemble approach: voting with 11 models using different emb. & epochs embedding (emb.) accuracy glove 0.9525 amazonwe 0.9122 emb_128_15 0.9578 emb_128_30 0.9560 emb_128_50 0.9544 emb_256_15 0.9576 emb_256_30 0.9576 emb_256_50 0.9578 emb_512_15 0.9568 emb_512_30 0.9598 emb_512_50 0.9605 ensemble approach 0.9657
  • 5. 5 Results on Test Set 5 test: 10,000 equally distributed over 20 domains team accuracy our approach 0.9643 Team#2 0.9561 Team#3 0.9356 Team#4 0.9228 Team#5 0.8823 Team#6 0.8743 … … … …
  • 6. funded by thank you for your attention! Guangyuan Piao, John G. Breslin, Domain-Aware Sentiment Classification with GRUs and CNNs, Semantic Sentiment Classification Challenge at ESWC2018, Crete, Greece Guangyuan Piao homepage: http://parklize.github.io e-mail: guangyuan.piao@insight-centre.org scholar: https://goo.gl/tgK9bk slideshare: http://www.slideshare.net/parklize