Sentiment analysis in social media is a popular task attracting the interest of the research community, also in recent evaluation campaigns of natural language processing tasks in sev- eral languages. We report on our experience in the organization of SENTIPOLC (SENTIment POLarity Classification Task), a shared task on sentiment classification of Italian tweets, proposed for the first time in 2014 within the Evalita evaluation campaign. We present the datasets – which include an enriched annotation scheme for dealing with the impact of figurative language on polarity – the evaluation methodology, and discuss the approaches and results of participating systems. We also offer a reflection on the open challenges of state-of-the-art systems for sentiment analysis of microblogging in Italian, as they emerge from a qualitative analysis of misclassified tweets. Finally, we provide an evaluation of the resources we have created, and share the lessons learned by running this task for two consecutive editions.
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|Titolo:||Sentiment Polarity Classification at EVALITA: Lessons Learned and Open Challenges|
|Data di pubblicazione:||2018|
|Appare nelle tipologie:||1.1 Articolo in rivista|