Fabio Celli


Multilevel Annotation of Agreement and Disagreement in Italian News Blogs
Fabio Celli | Giuseppe Riccardi | Firoj Alam
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

In this paper, we present a corpus of news blog conversations in Italian annotated with gold standard agreement/disagreement relations at message and sentence levels. This is the first resource of this kind in Italian. From the analysis of ADRs at the two levels emerged that agreement annotated at message level is consistent and generally reflected at sentence level, moreover, the argumentation structure of disagreement is more complex than agreement. The manual error analysis revealed that this resource is useful not only for the analysis of argumentation, but also for the detection of irony/sarcasm in online debates. The corpus and annotation tool are available for research purposes on request.

Predicting Brexit: Classifying Agreement is Better than Sentiment and Pollsters
Fabio Celli | Evgeny Stepanov | Massimo Poesio | Giuseppe Riccardi
Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES)

On June 23rd 2016, UK held the referendum which ratified the exit from the EU. While most of the traditional pollsters failed to forecast the final vote, there were online systems that hit the result with high accuracy using opinion mining techniques and big data. Starting one month before, we collected and monitored millions of posts about the referendum from social media conversations, and exploited Natural Language Processing techniques to predict the referendum outcome. In this paper we discuss the methods used by traditional pollsters and compare it to the predictions based on different opinion mining techniques. We find that opinion mining based on agreement/disagreement classification works better than opinion mining based on polarity classification in the forecast of the referendum outcome.

The Social Mood of News: Self-reported Annotations to Design Automatic Mood Detection Systems
Firoj Alam | Fabio Celli | Evgeny A. Stepanov | Arindam Ghosh | Giuseppe Riccardi
Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES)

In this paper, we address the issue of automatic prediction of readers’ mood from newspaper articles and comments. As online newspapers are becoming more and more similar to social media platforms, users can provide affective feedback, such as mood and emotion. We have exploited the self-reported annotation of mood categories obtained from the metadata of the Italian online newspaper corriere.it to design and evaluate a system for predicting five different mood categories from news articles and comments: indignation, disappointment, worry, satisfaction, and amusement. The outcome of our experiments shows that overall, bag-of-word-ngrams perform better compared to all other feature sets; however, stylometric features perform better for the mood score prediction of articles. Our study shows that self-reported annotations can be used to design automatic mood prediction systems.


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The Role of Emotional Stability in Twitter Conversations
Fabio Celli | Luca Rossi
Proceedings of the Workshop on Semantic Analysis in Social Media


UNITN: Part-Of-Speech Counting in Relation Extraction
Fabio Celli
Proceedings of the 5th International Workshop on Semantic Evaluation


Automatic identification of semantic relations in Italian complex nominals
Fabio Celli | Malvina Nissim
Proceedings of the Eight International Conference on Computational Semantics