Stance detection aims to automatically determine whether the author is in favor of or against a given target. In principle, the sentiment information of a post highly influences the stance. In this study, we aim to leverage the sentiment information of a post to improve the performance of stance detection. However, conventional discretemodels with sentimental features can cause error propagation. We thus propose a joint neural network model to predict the stance and sentiment of a post simultaneously, because the neural network model can learn both representation and interaction between the stance and sentiment collectively. Specifically, we first learn a deep shared representation between stance and sentiment information, and then use a neural stacking model to leverage sentimental information for the stance detection task. Empirical studies demonstrate the effectiveness of our proposed joint neural model.
S MMohammad, S Kiritchenko , PSobhani , XZhu , C Cherry . Semeval-2016 task 6: detecting stance in tweets. In: Proceedings of the 10th International Workshop on Semantic Evaluation. 2016, 31–41
2
SSomasundaran , JWiebe . Recognizing stances in online debates. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP. 2009, 226–234 https://doi.org/10.3115/1687878.1687912
3
AMurakami , R Raymond . Support or oppose?: classifying positions in online debates from reply activities and opinion expressions. In: Proceedings of the 23rd International Conference on Computational Linguistics. 2010, 869–875
4
PAnand , M Walker , RAbbott , J ETree , R Bowmani , MMinor . Cats rule and dogs drool!: classifying stance in online debate. In: Proceedings of the 2nd workshop on computational approaches to subjectivity and sentiment analysis. 2011, 1–9
5
M AWalker , PAnand , RAbbott , R Grant . Stance classification using dialogic properties of persuasion. In: Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2012, 592–596
6
K SHasan , VNg . Stance classification of ideological debates: data, models, features, and constraints. In: Proceedings of the International Joint Conference on Natural Language Processing. 2013, 1348–1356
7
QSun , ZWang , QZhu , G Zhou . Exploring various linguistic features for stance detection. In: Proceedings of the International Conference on Computer Processing of Oriental Languages. 2016, 840–847 https://doi.org/10.1007/978-3-319-50496-4_76
8
S MMohammad , P Sobhani , SKiritchenko . Stance and sentiment in tweets. 2016, arXiv preprint arXiv:1605.01655
9
MThomas , BPang , LLee . Get out the vote: determining support or opposition from congressional floor-debate transcripts. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing. 2006, 327–335 https://doi.org/10.3115/1610075.1610122
10
MBansal , C Cardie , LLee . The power of negative thinking: exploiting label disagreement in the min-cut classification framework. COLING 2008: Companion Volume: Posters. 2008, 15–18
11
CBurfoot , SBird , TBaldwin . Collective classification of congressional floor-debate transcripts. In: Proceedings of the 49th AnnualMeeting of the Association for Computational Linguistics. 2011, 1506–1515
12
RAgrawal , S Rajagopalan , RSrikant , YXu . Mining newsgroups using networks arising from social behavior. In: Proceedings of the 12th International Conference on World Wide Web. 2003, 529–535 https://doi.org/10.1145/775152.775227
13
DSridhar , L Getoor , MWalker . Collective stance classification of posts in online debate forums. In: Proceedings of the Joint Workshop on Social Dynamics and Personal Attributes in Social Media. 2014, 109–117 https://doi.org/10.3115/v1/W14-2715
14
KJohnson , D Goldwasser . Identifying stance by analyzing political discourse on twitter. In: Proceedings of EMNLP Workshop on Natural Language Processing and Computational Social Science. 2016, 66–75 https://doi.org/10.18653/v1/W16-5609
15
SVolkova , Y Bachrach , MArmstrong , VSharma . Inferring latent user properties from texts published in social media. In: Proceedings of Association for the Advancement of Artificial Intelligence. 2015, 4296–4297
16
MLukasik , P K Srijith , DVu , KBontcheva , A Zubiaga , TCohn . Hawkes processes for continuous time sequence classification: an application to rumour stance classification in twitter. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. 2016, 393–398 https://doi.org/10.18653/v1/P16-2064
17
AZubiaga , E Kochkina , MLiakata , RProcter , M Lukasik . Stance classification in rumours as a sequential task exploiting the tree structure of social media conversations. 2016, arXiv preprint arXiv:1609.09028
18
ARajadesingan , HLiu . Identifying users with opposing opinions in Twitter debates. In: Proceedings of the International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction. 2014, 153–160 https://doi.org/10.1007/978-3-319-05579-4_19
19
S MMohammad , S Kiritchenko , PSobhani , XZhu , C Cherry . A dataset for detecting stance in tweets. In: Proceedings of the 10th edition of the the Language Resources and Evaluation Conference (LREC). 2016, 3945–3952
20
GZarrella , AMarsh . MITRE at semeval-2016 task 6: transfer learning for stance detection. In: Proceedings of the 10th International Workshop on Semantic Evaluation. 2016, 458–463
21
WWei , XZhang , XLiu , W Chen , TWang . Pkudblab at semeval-2016 task 6: a specific convolutional neural network system for effective stance detection. In: Proceedings of the 10th International Workshop on Semantic Evaluation. 2016, 384–388
22
BPang , LLee , SVaithyanathan . Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2002, 79–86 https://doi.org/10.3115/1118693.1118704
23
AYessenalina , YYue , CCardie . Multi-level structured models for document-level sentiment classification. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing. 2010, 1046–1056
24
TBrychcın , I Habernal . Unsupervised improving of sentiment analysis using global target context. In: Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP. 2013, 122–128
25
DTang , BQin , TLiu . Document modeling with gated recurrent neural network for sentiment classification. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2015, 1422–1432 https://doi.org/10.18653/v1/D15-1167
26
AKhattri , AJoshi , PBhattacharyya , M JCarman . Your sentiment precedes you: using an author’s historical tweets to predict sarcasm. In: Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. 2015, 25–30 https://doi.org/10.18653/v1/W15-2905
27
HSaif , YHe , MFernandez , HAlani . Contextual semantics for sentiment analysis of Twitter. Information Processing & Management. 2016, 52(1): 5–19 https://doi.org/10.1016/j.ipm.2015.01.005
28
PSobhani , S M Mohammad , SKiritchenko . Detecting stance in tweets and analyzing its interaction with sentiment. In: Proceedings of the 5th Joint Conference on Lexical and Computational Semantics. 2016, 159–169 https://doi.org/10.18653/v1/S16-2021
29
ITitov , R T McDonald . A joint model of text and aspect ratings for sentiment summarization. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics. 2008, 308–316
30
YWatanabe , M Asahara , YMatsumoto . A structured model for joint learning of argument roles and predicate senses. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics. 2010, 98–102 https://doi.org/10.1527/tjsai.25.252
31
ISimova , D Vasilev , APopov , KSimov , P Osenova . Joint ensemble model for POS tagging and dependency parsing. In: Proceedings of the 1st Joint Workshop on Statistical Parsing of Morphologically Rich Languages and Syntactic Analysis of Non-Canonical Languages. 2014, 15–25
32
RSocher , A Perelygin , J YWu , JChuang , C D Manning , A YNg , CPotts . Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2013, 1631–1642
33
RCollobert , J Weston , LBottou , MKarlen , K Kavukcuoglu , PKuksa . Natural language processing (almost) from scratch. Journal of Machine Learning Research. 2011, 12(Aug): 2493–2537
34
YLiu , SLi , XZhang , Z Sui . Implicit discourse relation classification via multi-task neural networks. 2016, arXiv preprint arXiv:1603.02776
35
JZhou , WXu . End-to-end learning of semantic role labeling using recurrent neural networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. 2015, 1127–1137 https://doi.org/10.3115/v1/P15-1109
36
HChen , YZhang , QLiu . Neural network for heterogeneous annotations. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2016, 731–741 https://doi.org/10.18653/v1/D16-1070
RJohnson , TZhang . Effective use of word order for text categorization with convolutional neural networks. 2014, arXiv preprint arXiv:1412.1058
40
NSrivastava , G E Hinton , AKrizhevsky , ISutskever , R Salakhutdinov . Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 2014, 15(1): 1929–1958
41
TTieleman , G Hinton . Rmsprop: divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks for Machine Learning. Technical Report, 2012
42
XGlorot , Y Bengio . Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the 13th International Conference on Artifical Intelligence and Statistics. 2010, 249–256
43
TMikolov, KChen, GCorrado, J Dean. Efficient estimation of word representations in vector space. 2013, arXiv preprint arXiv:1301.3781
