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Sentiment Polarity Classification at EVALITA: Lessons Learned and Open Challenges
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Abstract:
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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Keyword:
Evaluation; Irony Detection; Sentiment Analysis; Social Media Analysis
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URL: http://hdl.handle.net/2318/1684800
https://doi.org/10.1109/TAFFC.2018.2884015
http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5165369
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Overview of the Evalita 2014 SENTIment POLarity Classification Task
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In: Proceedings of the 4th evaluation campaign of Natural Language Processing and Speech tools for Italian (EVALITA'14) ; https://hal.inria.fr/hal-01228925 ; Proceedings of the 4th evaluation campaign of Natural Language Processing and Speech tools for Italian (EVALITA'14), 2014, Pisa, Italy. ⟨10.12871/clicit201429⟩ (2014)
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