An LSTM-CNN attention approach for aspect-level sentiment classification

[1]  Haris Papageorgiou,et al.  SemEval-2016 Task 5: Aspect Based Sentiment Analysis , 2016, *SEMEVAL.

[2]  Yang Liu,et al.  Learning Tag Embeddings and Tag-specific Composition Functions in Recursive Neural Network , 2015, ACL.

[3]  Lukás Burget,et al.  Recurrent neural network based language model , 2010, INTERSPEECH.

[4]  Masaru Kitsuregawa,et al.  Building Lexicon for Sentiment Analysis from Massive Collection of HTML Documents , 2007, EMNLP.

[5]  Ting Liu,et al.  Aspect Level Sentiment Classification with Deep Memory Network , 2016, EMNLP.

[6]  Jeffrey Pennington,et al.  Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions , 2011, EMNLP.

[7]  Saif Mohammad,et al.  NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets , 2013, *SEMEVAL.

[8]  Sven Behnke,et al.  Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition , 2010, ICANN.

[9]  Li Zhao,et al.  Attention-based LSTM for Aspect-level Sentiment Classification , 2016, EMNLP.

[10]  Hua Xu,et al.  Chinese comments sentiment classification based on word2vec and SVMperf , 2015, Expert Syst. Appl..

[11]  Xiaocheng Feng,et al.  Effective LSTMs for Target-Dependent Sentiment Classification , 2015, COLING.

[12]  John G. Breslin,et al.  A Hierarchical Model of Reviews for Aspect-based Sentiment Analysis , 2016, EMNLP.

[13]  Mathieu Cliche,et al.  BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs , 2017, *SEMEVAL.

[14]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.