Francesco Barbieri

Modelling Sarcasm in Twitter, a Novel Approach

Automatic detection of figurative language is a challenging task in computational linguistics. Recognising both literal and figurative meaning is not trivial for a machine and in some cases it is hard even for humans. For this reason novel and accurate systems able to recognise figurative languages are necessary. We present in this paper a novel computational model capable to detect sarcasm in the social network Twitter (a popular microblogging service which allows users to post short messages). Our model is easy to implement and, unlike previous systems, it does not include patterns of words as features. Our seven sets of lexical features aim to detect sarcasm by its inner structure (for example unexpectedness, intensity of the terms or imbalance between registers), abstracting from the use of specific terms.

Modelling Irony in Twitter

Computational creativity is one of the central research topics of Artificial Intelligence and Natural Language Processing today. Irony, a creative use of language, has received very little attention from the computational linguistics research point of view. In this study we investigate the automatic detection of irony casting it as a classification problem. We propose a model capable of detecting irony in the social network Twitter. In cross-domain classification experiments our model based on lexical features outperforms a word-based baseline previously used in opinion mining and achieves state-of-the-art performance. Our features are simple to implement making the approach easily replicable.

How Cosmopolitan Are Emojis?: Exploring Emojis Usage and Meaning over Different Languages with Distributional Semantics

Choosing the right emoji to visually complement or condense the meaning of a message has become part of our daily life. Emojis are pictures, which are naturally combined with plain text, thus creating a new form of language. These pictures are the same independently of where we live, but they can be interpreted and used in different ways. In this paper we compare the meaning and the usage of emojis across different languages. Our results suggest that the overall semantics of the subset of the emojis we studied is preserved across all the languages we analysed. However, some emojis are interpreted in a different way from language to language, and this could be related to socio-geographical differences.

Revealing patterns of Twitter emoji usage in Barcelona and Madrid

Comunicacio presentada a: 19th International Conference of the Catalan Association for Artificial Intelligence, Barcelona, Catalonia, Spain, October 19–21, 2016

UPF-taln: SemEval 2015 Tasks 10 and 11. Sentiment Analysis of Literal and Figurative Language in Twitter

In this paper, we describe the approach used by the UPF-taln team for tasks 10 and 11 of SemEval 2015 that respectively focused on “Sentiment Analysis in Twitter” and “Sentiment Analysis of Figurative Language in Twitter”. Our approach achieved satisfactory results in the figurative language analysis task, obtaining the second best result. In task 10, our approach obtained acceptable performances. We experimented with both wordbased features and domain-independent intrinsic word features. We exploited two machine learning methods: the supervised algorithm Support Vector Machines for task 10, and Random-Sub-Space with M5P as base algorithm for task 11.