[1] PANG B,LEE L.Opinion mining and sentiment analysis[J].Foundations and Trends in Information Retrieval,2008,2(1/2):1-13. [2] CARVALHO P,SARMENTO L,SILVA M J,et al.Clues for detecting irony in user-generated contents:oh…!! it's "so easy"[C]//Proceedings of the 1st International CIKM Workshop on Topic-sentiment Analysis for Mass Opinion.New York,USA:ACM Press,2009:53-56. [3] RILOFF E,QADIR A,SURVE P,et al.Sarcasm as contrast between a positive sentiment and negative situation[C]//Proceedings of 2013 IEEE Conference on Empirical Methods in Natural Language Processing.Washington D.C.,USA:IEEE Press,2013:704-714. [4] BAMMAN D,SMITH N A.Contextualized sarcasm detection on twitter[C]//Proceedings of the 9th International AAAI Conference on Web and Social Media.[S.1.]:AAAI Press,2015:457-468. [5] AMIR S,WALLACE B C,LYU H,et al.Modelling context with user embeddings for sarcasm detection in social media[EB/OL].[2019-10-10].https://arxiv.xilesou.top/pdf/1607.00976.pdf. [6] HAZARIKA D,PORIA S,GORANTLA S,et al.CASCADE:contextual sarcasm detection in online discussion forums[EB/OL].[2019-10-10].https://arxiv.xilesou.top/pdf/1805.06413. [7] HOTELLING H.Relations between two sets of variates[J].Biometrika,1936,28(3/4):321-377. [8] KHODAK M,SAUNSHI N,VODRAHALLI K.A large self-annotated corpus for sarcasm[EB/OL].[2019-10-10].https://arxiv.xilesou.top/pdf/1704.05579.pdf. [9] LE Q,MIKOLOV T.Distributed representations of sentences and documents[C]//Proceedings of IEEE Inter-national Conference on Machine Learning.Washington D.C.,USA:IEEE Press,2014:1188-1196. [10] CHO K,VAN MERRIENBOER B,GULCEHRE C,et al.Learning phrase representations using RNN encoder-decoder for statistical machine translation[EB/OL].[2019-10-10].https://arxiv.xilesou.top/pdf/1406.1078.pdf. [11] MIKOLOV T,SYTSKEVER I,CHEN K,et al.Distributed representations of words and phrases and their com-positionality[J].Neural Information Processing Systems,2013,26:3111-3119. [12] LECUN Y,BEBGIO Y,HINTON G.Deep learning[J].Nature,2015,521(7553):436-444. [13] PORIN S,CAMBRIA E,HAZARIKA D,et al.A deeper look into sarcastic tweets using deep convolutional neural networks[EB/OL].[2019-10-10].https://arxiv.xilesou.top/pdf/1610.08815.pdf. [14] ZHANG Meishan,ZHANG Yue,FU Guohong.Tweet sarcasm detection using deep neural network[C]//Pro-ceedings of the 26th International Conference on Com-putational Linguistics:Technical Papers.Washington D.C.,USA:IEEE Press,2016:2449-2460. [15] GHOSH A,VEALE T.Fracking sarcasm using neural network[C]//Proceedings of the 7th IEEE Workshop on Computational Approaches to Subjectivity,Sentiment and Social Media Analysis.Washington D.C.,USA:IEEE Press,2016:161-169. [16] TAY Y,TUAN L A,HUI S C,et al.Reasoning with sarcasm by reading in-between[EB/OL].[2019-10-10].https://arxiv.xilesou.top/pdf/1805.02856.pdf. [17] GOLDBERG Y.Neural network methods for natural language processing[J].Human Language Technologies,2017,10(1):288-309. [18] BENGIO Y,DUCHARME R,VINCENT P,et al.A neural probabilistic language model[J].Journal of Machine Learning Research,2003,3:1137-1155. [19] PENNINGTON J,SOCHER R,MANNING C.GloVe:global vectors for word representation[C]//Proceedings of 2014 IEEE Conference on Empirical Methods in Natural Language Processing.Washington D.C.,USA:IEEE Press,2014:1532-1543. [20] KIM Y.Convolutional neural networks for sentence classification[EB/OL].[2019-10-10].https://arxiv.xilesou.top/pdf/1408.5882.pdf. |