Weakly-Supervised Deep Embedding for Product Review Sentiment Analysis
Tóm tắt
Từ khóa
Tài liệu tham khảo
socher, 2012, Semantic compositionality through recursive matrix-vector spaces, Proc Joint Conf Empirical Methods Natural Language Process Comput Natural Language Learn, 1201
mullen, 2004, Sentiment analysis using support vector machines with diverse information sources, Proc Conf Empirical Methods Natural Language Process, 4, 412
mikolov, 2013, Distributed representations of words and phrases and their compositionality, Proc Advances Neural Inf Process Syst, 3111
mikolov, 2013, Efficient estimation of word representations in vector space, Proc Workshop Int Conf Learn Represent
qu, 2012, A weakly supervised model for sentence-level semantic orientation analysis with multiple experts, Proc Joint Conf Empirical Methods Natural Language Process Comput Natural Language Learn, 149
qiu, 2015, Convolutional neural tensor network architecture for community-based question answering, Proc 24th Int Conf Artif Intell, 1305
maaten, 2008, Visualizing data using t-SNE, J Mach Learn Res, 9, 2579
maas, 2011, Learning word vectors for sentiment analysis, Proc Annu Meeting Assoc Comput Linguist Conf Human Lang Technol, 142
kiros et, 2015, Skip-thought vectors, Proc 28th Int Conf Neural Inf Process Syst, 3294
le, 2014, Distributed representations of sentences and documents, Proc Int Conf Mach Learn, 14, 1188
lakkaraju, 2014, Aspect specific sentiment analysis using hierarchical deep learning, Proc Neural Inf Process Syst Workshop Deep Learn Representation Learn
wieting, 2015, Towards universal paraphrastic sentence embeddings
zhu, 2011, Aspect-based opinion polling from customer reviews, IEEE Trans Affect Comput, 2, 37, 10.1109/T-AFFC.2011.2
zhang, 2015, Character-level convolutional networks for text classification, Proc 28th Int Conf Neural Inf Process Syst, 649
zhang, 2011, Identifying noun product features that imply opinions, Proc 32nd Ann Meeting Assoc for Computational Linguistics, 575
duchi, 2011, Adaptive subgradient methods for online learning and stochastic optimization, J Mach Learn Res, 12, 2121
fan, 2008, Liblinear: A library for large linear classification, J Mach Learn Res, 9, 1871
socher, 2011, Semi-supervised recursive autoencoders for predicting sentiment distributions, Proc Conf Empirical Methods Natural Language Process, 151
glorot, 2011, Domain adaptation for large-scale sentiment classification: A deep learning approach, Proc 28th Int Conf Mach Learn, 513
bishop, 2006, Pattern Recognition and Machine Learning
collobert, 2011, Natural language processing (almost) from scratch, J Mach Learn Res, 12, 2493
wang, 2012, Baselines and bigrams: Simple, good sentiment and topic classification, Proc 32nd Ann Meeting Assoc for Computational Linguistics, 90
turney, 2002, Thumbs up or thumbs down?: Semantic orientation applied to unsupervised classification of reviews, Proc 32nd Ann Meeting Assoc for Computational Linguistics, 417
täckström, 2011, Semi-supervised latent variable models for sentence-level sentiment analysis, Proc 46th Annu Meeting Assoc for Comput Linguist Human Lang Technol Short Papers, 569
socher, 2013, Recursive deep models for semantic compositionality over a sentiment treebank, Proc Conf Empirical Methods Natural Language Process, 1631
tang, 2016, Effective LSTMs for target-dependent sentiment classification, Proc 26th Int Conf Comput Linguistics, 3298