Developing Corpora for Sentiment Analysis: The Case of Irony and Senti-TUT

Senti-TUT-an ongoing Italian project that investigates sentiment and irony in online political discussions-illustrates how to develop corpora for mining and analyzing opinion and sentiment in social media.

[1]  Nick Cercone,et al.  Computational Linguistics , 1986, Communications in Computer and Information Science.

[2]  Saif Mohammad,et al.  Tracking Sentiment in Mail: How Genders Differ on Emotional Axes , 2011, WASSA@ACL.

[3]  Feiyu Xu,et al.  Annotating Opinions in German Political News , 2012, LREC.

[4]  Nicoletta Ide Nancy Calzolari,et al.  Language Resources and Evaluation , 1966 .

[5]  C. Pelachaud,et al.  Emotion-Oriented Systems: The Humaine Handbook , 2011 .

[6]  Kam-Fai Wong,et al.  Quantising Opinions for Political Tweets Analysis , 2012, LREC.

[7]  Erik Cambria,et al.  Sentic Computing: Techniques, Tools, and Applications , 2012 .

[8]  Claire Cardie,et al.  Annotating Expressions of Opinions and Emotions in Language , 2005, Lang. Resour. Evaluation.

[9]  Min-Yen Kan,et al.  Perspectives on crowdsourcing annotations for natural language processing , 2012, Language Resources and Evaluation.

[10]  Davide Buscaldi,et al.  From humor recognition to irony detection: The figurative language of social media , 2012, Data Knowl. Eng..

[11]  E. Cambria,et al.  Sentic Computing , 2015, Cognitive Computation.

[12]  Peter Kulchyski.  and , 2015 .

[13]  Erik Cambria,et al.  SenticNet 2: A Semantic and Affective Resource for Opinion Mining and Sentiment Analysis , 2012, FLAIRS.

[14]  Richard Johansson,et al.  Relational Features in Fine-Grained Opinion Analysis , 2013, CL.

[15]  Paolo Rosso,et al.  SemEval-2015 Task 11: Sentiment Analysis of Figurative Language in Twitter , 2015, *SEMEVAL.

[16]  Isabell M. Welpe,et al.  Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment , 2010, ICWSM.

[17]  K. Bretonnel Cohen,et al.  Sentiment Analysis of Suicide Notes: A Shared Task , 2012, Biomedical informatics insights.

[18]  Paolo Rosso,et al.  A multidimensional approach for detecting irony in Twitter , 2013, Lang. Resour. Evaluation.

[19]  Sanda M. Harabagiu,et al.  EmpaTweet: Annotating and Detecting Emotions on Twitter , 2012, LREC.

[20]  Rada Mihalcea,et al.  Multilingual Subjectivity: Are More Languages Better? , 2010, COLING.

[21]  Erik Cambria,et al.  The Hourglass of Emotions , 2011, COST 2102 Training School.

[22]  Diana Maynard,et al.  Who cares about Sarcastic Tweets? Investigating the Impact of Sarcasm on Sentiment Analysis. , 2014, LREC.

[23]  Luigi Di Caro,et al.  Annotating Irony in a Novel Italian Corpus for Sentiment Analysis , 2012 .

[24]  D. Maynard,et al.  Challenges in developing opinion mining tools for social media , 2012 .

[25]  Catherine Pelachaud,et al.  Emotion-Oriented Systems , 2011 .

[26]  Malvina Nissim,et al.  Overview of the Evalita 2014 SENTIment POLarity Classification Task , 2014 .

[27]  Ron Artstein,et al.  Survey Article: Inter-Coder Agreement for Computational Linguistics , 2008, CL.

[28]  Cristina Bosco,et al.  Detecting Happiness in Italian Tweets: Towards an Evaluation Dataset for Sentiment Analysis in Felicittà , 2014 .

[29]  Elena Filatova,et al.  Irony and Sarcasm: Corpus Generation and Analysis Using Crowdsourcing , 2012, LREC.

[30]  Ari Rappoport,et al.  Semi-Supervised Recognition of Sarcasm in Twitter and Amazon , 2010, CoNLL.

[31]  Saeedeh Momtazi,et al.  Fine-grained German Sentiment Analysis on Social Media , 2012, LREC.