607 research outputs found
TWITTIRÒ: an Italian Twitter Corpus with a Multi-layered Annotation for Irony
Provided the difficulties that still affect a correct identification of irony within the context of Sentiment Analysis tasks, in this paper we describe the main issues emerged during the development of a novel resource for Italian annotated for irony. The project mainly consists in the application on the Twitter corpus TWITTIRĂ’ of a multi-layered scheme for the fine-grained annotation of irony, as proposed in a multilingual setting and previously applied also on French and English datasets (Karoui et al. 2017). In applying the annotation on this corpus, we outline and discuss the issues and peculiarities emerged about the exploitation of the semantic scheme for Twitter textual messages in Italian, thus shedding some lights on the future directions that can be followed in the multilingual and cross-language perspective too. We present, in particular, an analysis of the annotation process and distribution of the labels of each layer involved in the scheme. This is supported by a discussion of the outcome of the annotation carried on by native Italian speakers in the development of the corpus. In particular, an in-depth discussion of the inter-annotator agreement and of the sources of disagreement is included. The result is a novel gold standard corpus for irony detection in Italian, which enriches the scenario of multilingual datasets available for this challenging task and is ready to be used as a benchmark in automatic irony detection experiments and evaluation campaigns
Marking Irony Activators in a Universal Dependencies Treebank: The Case of an Italian Twitter Corpus
Identifying Purpose Behind Electoral Tweets
Tweets pertaining to a single event, such as a national election, can number
in the hundreds of millions. Automatically analyzing them is beneficial in many
downstream natural language applications such as question answering and
summarization. In this paper, we propose a new task: identifying the purpose
behind electoral tweets--why do people post election-oriented tweets? We show
that identifying purpose is correlated with the related phenomenon of sentiment
and emotion detection, but yet significantly different. Detecting purpose has a
number of applications including detecting the mood of the electorate,
estimating the popularity of policies, identifying key issues of contention,
and predicting the course of events. We create a large dataset of electoral
tweets and annotate a few thousand tweets for purpose. We develop a system that
automatically classifies electoral tweets as per their purpose, obtaining an
accuracy of 43.56% on an 11-class task and an accuracy of 73.91% on a 3-class
task (both accuracies well above the most-frequent-class baseline). Finally, we
show that resources developed for emotion detection are also helpful for
detecting purpose
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