Sentiment analysis (or 'opinion mining') is the field of computational linguistics that deals with the automatic identification of users' emotions, evaluations, and subjective judgements in text. Due to the high diversity of the ways in which people can express their opinions, sentiment analysis is commonly considered to be a highly domain-dependent task. However, lexicon-based approaches can capture the domain-neutral sentiment words to a large extent.
Opinions can be analyzed at different levels of language granularity: for a complete text, a sentence, and on the subsentential level. Finally, a common distinction is between coarse-grained (determine just the polarity or 'semantic orientation' of a piece of text) and fine-grained (determine holder, target, and polarity of the opinion) analysis.
In our group, we work on both English and German and on a range of different domains and genres, including newspaper articles, product reviews and Twitter microblogs. The English work is a collaboration with the group of Prof. Maite Taboada (Simon Fraser Univ., Vancouver), who developed the SO-CAL system (Taboada et al. 2011), and we showed how it can be successfully merged with a document's genre structure analysis (Taboada et al. 2009).
For German, we currently focus on sentiment analysis for Twitter (Sidarenka 2016) and on methods for generation lexical resources for that task (Sidarenka/Stede 2016).
- Manfred Stede. Automatic argumentation mining and the role of stance and sentiment. Journal of Argumentation in Context, 9(1):19–41, 2020. [Bibtex]
- Maria Skeppstedt, Andreas Kerren, and Manfred Stede. Finding Reasons for Vaccination Hesitancy: Evaluating Semi-Automatic Coding of Internet Discussion Forums. In Proc. of MEDINFO. Lyon, 2019. URL: https://www.ncbi.nlm.nih.gov/pubmed/31437943. [Bibtex]
- Robin Schäfer and Manfred Stede. Improving implicit stance classification in tweets using word and sentence embeddings. In Christoph Benzm\"ullerand Heiner Stuckenschmidt, editor, KI 2019: Advances in Artificial Intelligence, 299–307. Cham, 2019. Springer International Publishing. URL: https://link.springer.com/chapter/10.1007/978-3-030-30179-8_26. [Bibtex]
- Maria Skeppstedt, Manfred Stede, and Andreas Kerren. Stance-taking in topics extracted from vaccine-related tweets and discussion forum posts. In Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop and Shared Task, 5–8. Brussels, 2018. Association for Computational Linguistics. [Bibtex] [PDF]
- Manfred Stede. Computerlinguistische Werkzeuge zur Analyse meinungsorientierter Texte: eine Fallstudie. In Hans W. Giessen and Hartmut E.H. Lenk, editors, Persuasionsstile in Europa III. Olms, Hildesheim, 2017. [Bibtex]
- Uladzimir Sidarenka. PotTS at GermEval-2017 Task B: Document-Level Polarity Detection Using Hand-Crafted SVM and Deep Bidirectional LSTM Network . In Proceedings of the GSCL GermEval Shared Task on Aspect-based Sentiment in Social Media Customer Feedback. Berlin, Germany, 2017. URL: https://drive.google.com/file/d/0B0IJZ0wwnhHDc1ZpcU05Mnh2N0U/view. [Bibtex]
- Núria Bertomeu Castelló. Extracting word lists for domain-specific implicit opinions from corpora. In International Workshop on Computational Semantics (IWCS). Avignon, France, September 2017. [Bibtex] [PDF]
- Maria Skeppstedt, Andreas Kerren, and Manfred Stede. Automatic detection of stance towards vaccination in online discussion forums. In International Workshop on Digital Disease Detection using Social Media (at IJCNLP). Taipei, Taiwan, November 2017. [Bibtex] [PDF]
- Katarina Krüger, Anna Lukowiak, Jonathan Sonntag, and Manfred Stede. Classifying news versus opinions in newspapers: Linguistic features for domain independence. Natural Language Enginnering, 23(5):687–707, 2017. URL: http://dx.doi.org/10.1017/S1351324917000043. [Bibtex]
- Uladzimir Sidarenka. PotTS at SemEval-2016 Task 4: Sentiment Analysis of Twitter Using Character-level Convolutional Neural Networks. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), 235–242. San Diego, California, June 2016. Association for Computational Linguistics. [Bibtex] [PDF]
- Leonard Kriese. System documentation for the IGGSA shared task 2016. In J. Ruppenhofer, J.M. Struß, and M. Wiegand, editors, IGGSA Shared Task on Source and Target Extraction from Political Speeches, Bochumer Linguistische Arbeitsberichte 18. Ruhr-Universität Bochum, 2016. URL: https://www.linguistics.rub.de/bla/018-steps2016.pdf. [Bibtex]
- Uladzimir Sidarenka and Manfred Stede. Generating Sentiment Lexicons for German Twitter. In Proceedings of the Workshop on Computational Modeling of People's Opinions, Personality, and Emotions in Social Media (PEOPLES 2016). Osaka, Japan, december 2016. [Bibtex] [PDF]
- Uladzimir Sidarenka. PotTS: The Potsdam Twitter Sentiment Corpus. In Nicoletta Calzolari et al., editor, Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC 2016). Portorož, Slovenia, may 2016. European Language Resources Association (ELRA). [Bibtex] [PDF]
- Jonathan Sonntag and Manfred Stede. Sentiment analysis: what’s your opinion? In Chris Biemann and Alexander Mehler, editors, Text Mining: From Ontology Learning to Automated Text Processing Applications. Springer, Berlin/Heidelberg/New York, 2014. [Bibtex]
- W. Sidorenko, J. Sonntag, M. Stede, N. Krüger, and S. Stieglitz. From newspaper to microblogging: what does it take to find opinions? In Proc. of 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media (WASSA), NAACL-HLT. Atlanta/GA, 2013. Association for Computational Linguistics. [Bibtex]
- Maite Taboada, Julian Brooke, Milan Tofiloski, Kimberly Voll, and Manfred Stede. Lexicon-based methods for sentiment analysis. Computational Linguistics, 37(2):267–307, 2011. URL: http://www.mitpressjournals.org/doi/pdf/10.1162/COLI_a_00049. [Bibtex]
- Maite Taboada, Julian Brooke, and Manfred Stede. Genre-based paragraph classification for sentiment analysis. In Proc. of the SIGDIAL 2009 Conference, 62–70. London, UK, September 2009. Association for Computational Linguistics. [Bibtex]