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SemEval-2014 Task 9: Sentiment Analysis in Twitter
Sara Rosenthal, Preslav Nakov, Alan Ritter +1
We describe the Sentiment Analysis in Twitter task, ran as part of SemEval-2014. It is a continuation of the last year's task that ran successfully as part of SemEval-2013. As in 2…
SemEval-2016 Task 4: Sentiment Analysis in Twitter
Preslav Nakov, Alan Ritter, Sara Rosenthal +2
This paper discusses the fourth year of the ``Sentiment Analysis in Twitter Task''. SemEval-2016 Task 4 comprises five subtasks, three of which represent a significant departure fr…
Word-Class Embeddings for Multiclass Text Classification
Alejandro Moreo, Andrea Esuli, Fabrizio Sebastiani
Pre-trained word embeddings encode general word semantics and lexical regularities of natural language, and have proven useful across many NLP tasks, including word sense disambigu…
Evaluating Variable-Length Multiple-Option Lists in Chatbots and Mobile Search
Pepa Atanasova, Georgi Karadzhov, Yasen Kiprov +2
In recent years, the proliferation of smart mobile devices has lead to the gradual integration of search functionality within mobile platforms. This has created an incentive to mov…
Cross-Lingual Sentiment Quantification
Andrea Esuli, Alejandro Moreo, Fabrizio Sebastiani
\emph{Sentiment Quantification} (i.e., the task of estimating the relative frequency of sentiment-related classes -- such as \textsf{Positive} and \textsf{Negative} -- in a set of…
Building Automated Survey Coders via Interactive Machine Learning
Andrea Esuli, Alejandro Moreo, Fabrizio Sebastiani
Software systems trained via machine learning to automatically classify open-ended answers (a.k.a. verbatims) are by now a reality. Still, their adoption in the survey coding indus…
Learning to Weight for Text Classification
Alejandro Moreo Fernández, Andrea Esuli, Fabrizio Sebastiani
In information retrieval (IR) and related tasks, term weighting approaches typically consider the frequency of the term in the document and in the collection in order to compute a…
Funnelling: A New Ensemble Method for Heterogeneous Transfer Learning and its Application to Cross-Lingual Text Classification
Andrea Esuli, Alejandro Moreo, Fabrizio Sebastiani
Cross-lingual Text Classification (CLC) consists of automatically classifying, according to a common set C of classes, documents each written in one of a set of languages L, and do…