sentiment detection; opinion mining; polarity detection; subjectivity analysis; relational learning (application of); global optimisation; machine learning (application of); Word Polarity; Machine Learning; Computational Linguistics; Sentiment Analysis; Relational Learning; Integer Linear Programming; Statistical NLP
Rentoumi V, Petrakis S, Karkaletsis V, Klenner M, Vouros G A, A collaborative system for sentiment analysis, in 6th Hellenic Conference on Artificial Intelligence (SETN 2010)
Klenner M, Petrakis S, Fahrni A, A tool for polarity classification of human affect from panel group texts, in International Conference on Affective Computing & Intelligent Interaction (ACII 2009)
Petrakis S, Klenner M, Ailloud É, Fahrni A, Composition multilingue de sentiments, in Traitement Automatique des Langues Naturelles (TALN 2009)
, Senlis, France.
Klenner M, Clematide S, Petrakis S, Luder M, Compositional syntax-based phrase-level polarity annotation for German, in The 10th International Workshop on Treebanks and Linguistic Theories (TLT 2012)
Clematide S, Klenner M, Evaluation and extension of a polarity lexicon for German, in Proceedings of the 1st Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (
, Lisbon7-13, 7-13.
Petrakis S, Klenner M, Learning theories for noun-phrase sentiment composition, in 8th International NLPCS Workshop
, Copenhagen(41), 179-188, Samfundslitteratur, (41), 179-188.
Clematide Simon, Gindl Stefan, Klenner Manfred, Petrakis Stefanos, Remus Robert, Ruppenhofer Josef, Waltinger Ulli, Wiegand Michael, MLSA - A Multi-layered Reference Corpus for German Sentiment Analysis, in Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2010)
Klenner Manfred, Petrakis Stefanos, Polarity preference of verbs: What could verbs reveal about the polarity of their objects?, in NLDB 2012
Klenner M, Fahrni A, Petrakis S, PolArt: a robust tool for sentiment analysis, in 17th Nordic Conference on Computational Linguistics (NODALIDA 2009)
, Odense, Denmark.
Klenner M, Petrakis S, Fahrni A, Robust compositional polarity classification, in Recent Advances in Natural Language Processing (RANLP 2009)
, Borovets, Bulgaria.
Klenner M, Süsse Beklommenheit und schmerzvolle Ekstase: Automatische Sentimentanalyse in den Werken von Eduard von Keyserling, in Christian Chiarcos Richard Eckart de Castilho und Manfred Stede (ed.), Narr Francke Attempto Verlag GmbH, Tübingen, 91-97.
Rentoumi V, Petrakis S, Klenner M, Vouros G A, Karkaletsis V, United we stand: improving sentiment analysis by joining machine learning and rule based methods, in 7th International Conference on Language Resources and Evaluation (LREC 2010)
The proposed project aims at the conceptualisation and implementation of a system carrying out bi-directional sentiment composition. The term 'bi-directional' is meant to denote the insight that the polarity (positive, negative or neutral) at the document-level depends on but at the same time restricts the polarities at the sentence-level. Positive and negative evaluations at the text-level do not alternate out of a sudden, rather they are rhetorically indicated (e.g. 'but', 'however'). So two neighbouring segments separated by a valency shifter (e.g. 'but') must have inverse polarities. This global constraint might induce a polarity shift in a top down manner forcing an otherwise bottom up operating sentiment composition to revise its decisions.We propose a new architecture for sentiment composition. At the document level, a constraint-based optimisation approach is taken that allows for global constraints. The sentence- and phrase-level sentiment theory is formulated as a Stochastic Logic Program (SLP). This SLP has to be learned from preclassified data and has to be tuned by the parameter estimation part of a Relational Learner. It represents the reliability of the whole process of sentiment composition, which depends on the quality of the parse trees and the various lexical resources for sentiment analysis that we plan to integrate. We also introduce a measure for the polarity strength that a particular SLP derivation bears. This way, we can answer the question how good or how bad an evaluation is, but also how sure we actually are about it.