A novel approach to generate a large scale of supervised data for short text sentiment analysis (original) (raw)

• Chen H (2013) Classification of commodity evaluation based on parsing. Shanghai Jiao Tong University
• Collobert R et al (2011) Natural language processing (almost) from scratch. J Mach Learn Res 12(1):2493–2537
  MATH Google Scholar
• Fawzi A et al (2016) Adaptive data augmentation for image classification. In: IEEE international conference on image processing IEEE, pp 3688–3692
• Glover J (2016) Modeling documents with generative adversarial networks. arXiv:1612.09122
• Goodfellow IJ, Pouget-Abadie J, Mirza M et al (2014) Generative adversarial Networks[J]. Adv Neural Inf Proces Syst 3:2672–2680
  Google Scholar
• Graves A, Schmidhuber J (2005) Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw 18 (5–6):602–610
  Article Google Scholar
• Hochreiter S, Schmidhuber J (2012) Long short-term memory. Neural Comput 9(8):1735
  Article Google Scholar
• Hua L (2014) Study on chinese text sentiment classification. Chongqing University
• Kalchbrenner N, Grefenstette E, Blunsom P (2014) A convolutional neural network for modelling sentences. Eprint Arxiv 1
• Karpathy A, Johnson J, Fei-Fei L (2015) Visualizing and understanding recurrent networks. arXiv:1506.02078
• Kim Y (2014) Convolutional neural networks for sentence classification. Eprint Arxiv
• Kingma DP, Welling M (2013) Auto-encoding variational bayes. arXiv:1312.6114
• Kiritchenko S et al (2014) NRC-Canada-2014: detecting aspects and sentiment in customer reviews. In: International workshop on semantic evaluation, pp 437–442
• Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: International conference on neural information processing systems curran associates inc., pp 1097–1105
• Le QV, Mikolov T (2014) Distributed representations of sentences and documents. Computer Science 4:1188–1196
  Google Scholar
• Lipton ZC, Berkowitz J, Elkan C (2015) A critical review of recurrent neural networks for sequence learning. arXiv:1506.00019
• Pang B, Lee L (2005) Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. In: Meeting on association for computational linguistics association for computational linguistics, pp 115–124
• Pang B, Lee L (2008) Opinion mining and sentiment analysis. Found Trends Inf Retr 2(1–2):1–135
  Article Google Scholar
• Rezende DJ, Mohamed S, Wierstra D (2014) Stochastic backpropagation and approximate inference in deep generative Models[J]. Eprint Arxiv, 1278–1286
• Rosario B, Hearst MA (2004) Classifying semantic relations in bioscience text. Meeting of the association for computational linguistics, 21-26 July, 2004, Barcelona, Spain DBLP, 430–437
• Ruder S, Ghaffari P, Breslin JG (2016) Insight-1 at semeval-2016 task 5: Deep learning for multilingual aspect-based sentiment analysis. arXiv:1609.02748
• Xiang R, Sun M (2016) Sentiment analysis of Chinese sentences based on word embedding and syntax tree[J]. Computer and Modernization 8:27–31
• Russell EWB (2015) Real-time topic and sentiment analysis in human-robot conversation. Dissertations & Theses - Gradworks
• Salamon J, Bello J (2016) Deep convolutional neural networks and data augmentation for environmental sound classification. IEEE Signal Process Lett 99:1–1
  Google Scholar
• Srivastava N et al (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958
  MathSciNet MATH Google Scholar
• Sun X, Li C, Ren F (2016) Sentiment analysis for Chinese microblog based on deep neural networks with convolutional extension features. Neurocomputing 210:227–236
  Article Google Scholar
• Sun X, Pan D, Ren F (2016) Facial expression recognition using ROI-KNN deep convolutional neural networks. Automation Journal 42(6):883–891
  Google Scholar
• Tang D, Qin B, Liu T (2016) Aspect level sentiment classification with deep memory network. arXiv:1605.08900
• Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural Netw 10(10):988–999
  Article Google Scholar
• Wang KK (2015) Image Classification with Pyramid Representation and Rotated Data Augmentation on Torch 7 [EB/OL]. https://hgpu.org/?p=13858
• Wang J et al (2016) Dimensional sentiment analysis using a regional CNN-LSTM model. In: Meeting of the association for computational linguistics, pp 225–230
• Yu L, Zhang W, Wang J et al (2017) SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient[C]. AAAI, 2852–2858
• Zhang L, Han Y, Yang Y, Song M, Yan S, Tian Q (2013) Discovering discriminative graphlets for aerial image categories recognition. IEEE T-IP 22(12):5071–5084
  Article MathSciNet Google Scholar
• Zhang L, Gao Y, Hong C, Feng Y, Zhu J, Cai D (2014) Feature correlation hypergraph: exploiting high-order potentials for multimodal recognition. IEEE T-CYB 44(8):1408–1419
  Google Scholar
• Zhang L, Gao Y, Ji R, Dai Q, Li X (2014) Actively learning human gaze shifting paths for photo cropping. IEEE T-IP 23(5):2235–2245
  Article MathSciNet Google Scholar
• Zhang L, Song M, Yang Y, Zhao Q, Zhao C, Sebe N (2014) Weakly supervised photo cropping. IEEE T-MM 16(1):94–107
  Google Scholar
• Zhang L, Hong R, Gao Y, Ji R, Dai Q, Li X (2016) Image categorization by learning a propagated graphlet path. IEEE T-NNLS 27(3):674–685
  MathSciNet Google Scholar
• Zhang L, Li X, Nie L, Yan Y (2016) Roger zimmermann, semantic photo retargeting under noisy image labels. ACM TOMCCAP 12(3):37
  Google Scholar
• Zhang L, Wang M, Hong R, Yin B -C, Li X (2016) Large-scale aerial image categorization using a multitask topological codebook. IEEE T-CYB 46 (2):535–545
  Google Scholar
• Zhang X, Lecun Y (2015) Text understanding from scratch. arXiv:1502.01710
• Zhang Y, Marshall I, Wallace BC (2016) Rationale-augmented convolutional neural networks for text classification. EMNLP 2016:795
  Google Scholar
• Zhou C et al (2015) A c-LSTM neural network for text classification. Computer Science 1(4):39–44
  MathSciNet Google Scholar