Cover not available
Article published In:
Argumentation and Meaning: Semantic and pragmatic reflexions
Edited by Steve Oswald, Sara Greco, Johanna Miecznikowski, Chiara Pollaroli and Andrea Rocci
[Journal of Argumentation in Context 9:1] 2020
► pp. 19–41

Automatic argumentation mining and the role of stance and sentiment

| University of Potsdam
Argumentation mining is a subfield of Computational Linguistics that aims (primarily) at automatically finding arguments and their structural components in natural language text. We provide a short introduction to this field, intended for an audience with a limited computational background. After explaining the subtasks involved in this problem of deriving the structure of arguments, we describe two other applications that are popular in computational linguistics: sentiment analysis and stance detection. From the linguistic viewpoint, they concern the semantics of evaluation in language. In the final part of the paper, we briefly examine the roles that these two tasks play in argumentation mining, both in current practice, and in possible future systems.
Keywords: argumentation structure, argumentation mining, sentiment analysis, stance detection
Article outline
  • 1.Introduction
  • 2.Some applications of argumentation mining in text
  • 3.Representing the structure of argumentation
  • 4.Argumentation mining: Subtasks
    • 4.1Find claims
    • 4.2Find premises
    • 4.3Build a representation of argumentation structure
  • 5.Capturing evaluation: Automatic stance detection and sentiment analysis
    • 5.1Stance detection
    • 5.2Sentiment analysis
  • 6.Sentiment and stance in argumentation mining
  • 7.Conclusions
  • Acknowledgements
  • Notes
  • References
Published online: 4 May 2020
DOI logo
https://doi.org/10.1075/jaic.00006.ste
References (36)
References
Afantenos, Stergos, Andreas Peldszus, and Manfred Stede. 2018. “Comparing decoding mechanisms for parsing argumentative structures”. Journal of Argumentation and Computation 9(3): 177–192. DOI logoGoogle Scholar
Al-Khatib, Khalid, Henning Wachsmuth, Johannes Kiesel, Matthias Hagen and Benno Stein. 2016. “A News Editorial Corpus for Mining Argumentation Strategies”. Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: 3433–3443, Osaka, Japan.Google Scholar
Bar-Haim, Roy, Lilach Edelstein, Charles Jochim, Noam Slonim. 2017. "Improving Claim Stance Classification with Lexical Knowledge Expansion and Context Utilization". Proceedings of the 4th Workshop on Argument Mining, pages 32–38, Copenhagen, Denmark. DOI logoGoogle Scholar
Biran, Or and Owen Rambow. 2011. “Identifying Justifications in Written Dialogs by Classifying Text as Argumentative.” Int. J. Semantic Computing 51 (2011): 363-381.Google Scholar
Boltuzic, Filip and Jan Snajder. 2016. *Fill the gap! Analyzing implicit premises between claims from online debates”. Proceedings of the Third Workshop on Argumentation Mining: 124–133, Berlin, Germany. DOI logoGoogle Scholar
Cohen, Robin. 1987. “Analyzing the Structure of Argumentative Discourse”. Computational Linguistics 13(1–2):11–24.Google Scholar
Deng, Lingjia and Janyce Wiebe. 2014. “Sentiment propagation via implicature constraints”. Proceedings of the 14th Conference of the European Chapter of the ACL: 377–385, Gothenburg, Sweden.Google Scholar
Eckle-Kohler, Judith, Roland Kluge, and Iryna Gurevych. 2015. “On the role of discourse markers for discriminating claims and premises in argumentative discourse”. Proceedings Empirical Methods in Natural Language Processing: 2236–2242, Lisbon, Portugal.Google Scholar
Falakmasir, Mohammad Hassan, Kevin D. Ashley, Christian D. Schunn, Diane J. Litman. 2014. “Identifying Thesis and Conclusion Statements in Student Essays to Scaffold Peer Review.” In: Trausan-Matu S., Boyer K.E., Crosby M., Panourgia K. (eds) Intelligent Tutoring Systems. ITS 2014. Lecture Notes in Computer Science, vol 84741. Springer, Cham. DOI logoGoogle Scholar
Fellbaum, Christiane (ed). 1998. WordNet: An Electronic Lexical Database. Cambridge, Massachusetts: MIT Press. DOI logoGoogle Scholar
Feng, Vanessa Wei and Graeme Hirst. 2011. “Classifying arguments by scheme”. Proceedings Association for Computational Linguistics: Human Language Technologies: 987–996, Portland, Oregon.Google Scholar
Freeman, James B. 2011. Argument Structure: Representation and Theory. Amsterdam: Springer. DOI logoGoogle Scholar
Habernal, Ivan and Iryna Gurevych. 2017. “Argumentation mining in user-generated web discourse”. Computational Linguistics 43(1): 125–179. DOI logoGoogle Scholar
Hasan, Kazi Saidul and Vincent Ng. 2013. “Stance classification of ideological debates: Data, models, features, and constraints”. Proceedings of the Sixth International Joint Conference on Natural Language Processing: 1348–1356, Nagoya, Japan.Google Scholar
Klenner, Manfred and Michael Amsler. 2016. “Sentiframes: a resource for verb-centered German sentiment inference”. Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC): 2888–2891, Portoroz, Slovenia.Google Scholar
Lawrence, John and Chris Reed. 2017. “Using Complex Argumentative Interactions to Reconstruct the Argumentative Structure of Large-Scale Debates”. 4th Workshop on Argumentation Mining: 108–117, Copenhagen, Denmark. DOI logoGoogle Scholar
Lippi, Marco and Paolo Torroni. 2016. “Argumentation mining: State of the art and emerging trends”. ACM Transactions on Internet Technology 16(2):101:1–10:25. DOI logoGoogle Scholar
Liu, Bing. 2012. Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies. San Rafael (CA): Morgan and Claypool.Google Scholar
Mohammad, Saif M., Svetlana Kiritchenko, Parinaz Sobhani, Xiaodan Zhu, Colin Cherry. 2016. “SemEval-2016 Task 6: Detecting Stance in Tweets”. Proceedings of SemEval 2016: 31–41, San Diego, California.Google Scholar
Palau and Moens 2009: Mochales Palau, Raquel and Marie-Francine Moens. 2009. "Argumentation mining: the detection, classification and structure of arguments in text." Proceedings of the 12th international conference on artificial intelligence and law, pages 98–107, New York.Google Scholar
Peldszus, Andreas and Manfred Stede. 2013. “From argument diagrams to argumentation mining in texts: A survey”. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 7(1):1–31. DOI logoGoogle Scholar
. 2016. “An annotated corpus of argumentative microtexts”. In Argumentation and Reasoned Action: Proceedings of 1st European Conference on Argumentation (Vol II), ed. By Dima Mohammed and Marcin Lewiński, 801–816, London: College Publications.Google Scholar
Persing, Isaac and Vincent Ng. 2015. “Modeling argument strength in student essays”. Proceedings 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers): 543–552, Beijing, China.Google Scholar
Shnarch, Eyal, Ran Levy, Vikas Raykar, and Noam Slonim. 2017. “Grasp: Rich patterns for argumentation mining”. Proceedings Empirical Methods in Natural Language Processing: 1356–1361, Copenhagen, Denmark.Google Scholar
Skeppstedt, Maria, Andreas Peldszus, and Manfred Stede. 2018. “More or less controlled elicitation of argumentative text: enlarging a microtext corpus via crowdsourcing”. Proceedings of the 5th Workshop on Argumentation Mining: 155–163. Brussels, Belgium. DOI logoGoogle Scholar
Socher, Richard, Alex Perelygin, Jean Wu, Jason Chuang, Christopher Manning, Andrew Ng and Christopher Potts. 2013. “Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank”. Proceedings of the Conference on Empirical Methods in Natural Language Processing: 1631–1642, Seattle, WA.Google Scholar
Somasundaran, Swapna and Janyce Wiebe. 2010. “Recognizing stances in ideological online debates”. Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text: 116–124, Los Angeles, CA.Google Scholar
Stab, Christian and Iryna Gurevych. 2014. “Annotating argument components and relations in persuasive essays”. Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: 1501–1510, Dublin, Ireland.Google Scholar
. 2017. “Parsing argumentation structures in persuasive essays”. Computational Linguistics, 43(3):619–660. DOI logoGoogle Scholar
Stede, Manfred and Jodi Schneider. 2018. Argumentation Mining. San Rafael (CA): Morgan and Claypool. DOI logoGoogle Scholar
Taboada, Maite, Julian Brooke, Milan Tofiloski, Kimberly Voll and Manfred Stede. 2011. “Lexicon-based methods for sentiment analysis”. Computational Linguistics 37(2):267–307. DOI logoGoogle Scholar
Toulmin, Stephen. 2008. “The layout of arguments”. In Reasoning: Studies of human inference and its foundations, ed. by Jonathan E. Adler and Lance J. Rips, 652–677, Cambridge: Cambridge University Press. DOI logoGoogle Scholar
Wachsmuth, Henning and Benno Stein. 2017. "A Universal Model for Discourse-Level Argumentation Analysis." In: Special Section of the ACM Transactions on Internet Technology: Argumentation in Social Media, 17 (3): 28:1–28:24, June 2017. DOI logoGoogle Scholar
Wachsmuth, Henning, Martin Potthast, Khalid Al-Khatib, Yamen Ajjour, Jana Puschmann, Jiani Qu, Jonas Dorsch, Viorel Morari, Janek Bevendorff, and Benno Stein. 2017a. “Building an argument search engine for the web”. Proceedings of the 4th Workshop on Argumentation Mining: 49–59, Copenhagen, Denmark. DOI logoGoogle Scholar
Wachsmuth, Henning, Nona Naderi, Yufang Hou, Yonatan Bilu, Vinodkumar Prabhakaran, Tim Alberdingk Thijm, Graeme Hirst, and Benno Stein. 2017b. “Computational Argumentation Quality Assessment in Natural Language”. Proceedings European Chapter of the Association for Computational Linguistics: 176–187, Valencia, Spain.Google Scholar
Wojatzki, Michael and Torsten Zesch. 2016. “Stance-based Argument Mining – Modeling Implicit Argumentation Using Stance”. Proceedings of the German Conference on Natural Language Processing KONVENS: 313–322, Bochum, Germany.Google Scholar
Cited by (3)

Cited by three other publications

Konat, Barbara, Ewelina Gajewska & Wiktoria Rossa
2024. Pathos in Natural Language Argumentation: Emotional Appeals and Reactions. Argumentation 38:3 ► pp. 369 ff. DOI logo
Green, Nancy L.
2023. The use of antithesis and other contrastive relations in argumentation. Argument & Computation 14:1 ► pp. 1 ff. DOI logo
Skiera, Bernd, Shunyao Yan, Johannes Daxenberger, Marcus Dombois & Iryna Gurevych
2022. Using Information-Seeking Argument Mining to Improve Service. Journal of Service Research 25:4 ► pp. 537 ff. DOI logo

This list is based on CrossRef data as of 19 october 2024. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers. Any errors therein should be reported to them.