Stefanos Petrakis


MLSA — A Multi-layered Reference Corpus for German Sentiment Analysis
Simon Clematide | Stefan Gindl | Manfred Klenner | Stefanos Petrakis | Robert Remus | Josef Ruppenhofer | Ulli Waltinger | Michael Wiegand
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

In this paper, we describe MLSA, a publicly available multi-layered reference corpus for German-language sentiment analysis. The construction of the corpus is based on the manual annotation of 270 German-language sentences considering three different layers of granularity. The sentence-layer annotation, as the most coarse-grained annotation, focuses on aspects of objectivity, subjectivity and the overall polarity of the respective sentences. Layer 2 is concerned with polarity on the word- and phrase-level, annotating both subjective and factual language. The annotations on Layer 3 focus on the expression-level, denoting frames of private states such as objective and direct speech events. These three layers and their respective annotations are intended to be fully independent of each other. At the same time, exploring for and discovering interactions that may exist between different layers should also be possible. The reliability of the respective annotations was assessed using the average pairwise agreement and Fleiss' multi-rater measures. We believe that MLSA is a beneficial resource for sentiment analysis research, algorithms and applications that focus on the German language.


United we Stand: Improving Sentiment Analysis by Joining Machine Learning and Rule Based Methods
Vassiliki Rentoumi | Stefanos Petrakis | Manfred Klenner | George A. Vouros | Vangelis Karkaletsis
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

In the past, we have succesfully used machine learning approaches for sentiment analysis. In the course of those experiments, we observed that our machine learning method, although able to cope well with figurative language could not always reach a certain decision about the polarity orientation of sentences, yielding erroneous evaluations. We support the conjecture that these cases bearing mild figurativeness could be better handled by a rule-based system. These two systems, acting complementarily, could bridge the gap between machine learning and rule-based approaches. Experimental results using the corpus of the Affective Text Task of SemEval ’07, provide evidence in favor of this direction.


PolArt: A Robust Tool for Sentiment Analysis
Manfred Klenner | Angela Fahrni | Stefanos Petrakis
Proceedings of the 17th Nordic Conference of Computational Linguistics (NODALIDA 2009)

Composition multilingue de sentiments
Stefanos Petrakis | Manfred Klenner | Étienne Ailloud | Angela Fahrni
Actes de la 16ème conférence sur le Traitement Automatique des Langues Naturelles. Démonstrations

Nous présentons ici PolArt, un outil multilingue pour l’analyse de sentiments qui aborde la composition des sentiments en appliquant des transducteurs en cascade. La compositionnalité est assurée au moyen de polarités préalables extraites d’un lexique et des règles de composition appliquées de manière incrémentielle.

Robust Compositional Polarity Classification
Manfred Klenner | Stefanos Petrakis | Angela Fahrni
Proceedings of the International Conference RANLP-2009