TDParse: Multi-target-specific sentiment recognition on Twitter
暂无分享,去创建一个
Arkaitz Zubiaga | Bo Wang | Maria Liakata | Rob Procter | Maria Liakata | Bo Wang | A. Zubiaga | R. Procter
[1] Noah A. Smith,et al. A Dependency Parser for Tweets , 2014, EMNLP.
[2] Yue Zhang,et al. Gated Neural Networks for Targeted Sentiment Analysis , 2016, AAAI.
[3] Chih-Jen Lin,et al. LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..
[4] Anna Rumshisky,et al. TwitterHawk: A Feature Bucket Based Approach to Sentiment Analysis , 2015, *SEMEVAL.
[5] Rada Mihalcea,et al. Graph-based Ranking Algorithms for Sentence Extraction, Applied to Text Summarization , 2004, ACL.
[6] Arkaitz Zubiaga,et al. WarwickDCS: From Phrase-Based to Target-Specific Sentiment Recognition , 2015, SemEval@NAACL-HLT.
[7] Patrick Paroubek,et al. Twitter as a Corpus for Sentiment Analysis and Opinion Mining , 2010, LREC.
[8] Shrikanth S. Narayanan,et al. A System for Real-time Twitter Sentiment Analysis of 2012 U.S. Presidential Election Cycle , 2012, ACL.
[9] Christopher D. Manning,et al. Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling , 2005, ACL.
[10] Maria Leonor Pacheco,et al. of the Association for Computational Linguistics: , 2001 .
[11] Ming Zhou,et al. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification , 2014, ACL.
[12] J. R. Landis,et al. The measurement of observer agreement for categorical data. , 1977, Biometrics.
[13] Isabell M. Welpe,et al. Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment , 2010, ICWSM.
[14] Eduard H. Hovy,et al. When Are Tree Structures Necessary for Deep Learning of Representations? , 2015, EMNLP.
[15] Haris Papageorgiou,et al. SemEval-2016 Task 5: Aspect Based Sentiment Analysis , 2016, *SEMEVAL.
[16] Xiaocheng Feng,et al. Effective LSTMs for Target-Dependent Sentiment Classification , 2015, COLING.
[17] Rachel Gibson,et al. 140 Characters to Victory?: Using Twitter to Predict the UK 2015 General Election , 2015, ArXiv.
[18] Craig MacDonald,et al. Comparing Overall and Targeted Sentiments in Social Media during Crises , 2016, ICWSM.
[19] Oren Etzioni,et al. Named Entity Recognition in Tweets: An Experimental Study , 2011, EMNLP.
[20] Yue Zhang,et al. Target-Dependent Twitter Sentiment Classification with Rich Automatic Features , 2015, IJCAI.
[21] Shubham Pateria,et al. AKTSKI at SemEval-2016 Task 5: Aspect Based Sentiment Analysis for Consumer Reviews , 2016, SemEval@NAACL-HLT.
[22] Stefan Evert,et al. KLUEless: Polarity Classification and Association , 2015, *SEMEVAL.
[23] Preslav Nakov,et al. SemEval-2015 Task 10: Sentiment Analysis in Twitter , 2015, *SEMEVAL.
[24] Ting Liu,et al. Aspect Level Sentiment Classification with Deep Memory Network , 2016, EMNLP.
[25] Preslav Nakov,et al. SemEval-2016 Task 4: Sentiment Analysis in Twitter. , 2019 .
[26] Ming Zhou,et al. Adaptive Recursive Neural Network for Target-dependent Twitter Sentiment Classification , 2014, ACL.
[27] Steven Bird,et al. NLTK: The Natural Language Toolkit , 2002, ACL.
[28] Tiejun Zhao,et al. Target-dependent Twitter Sentiment Classification , 2011, ACL.
[29] Jeffrey Dean,et al. Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.