Fuzzy linguistic aggregation to synthesize the Hourglass of Emotions

Emotions govern all the human actions and play a key role in decision-making processes. Capturing sentiments and opinions hidden in the written (natural) language is a key activity which attracts both the scientific community, by leading to many novel challenges, and the business world, by supporting market behavior and prediction. Sentiment analysis and Sentic Computing are two interrelated research trends that, by exploiting the common sense in the natural language, try to distill human feelings in the textual data.

[1]  B. Gu,et al.  The impact of online user reviews on hotel room sales , 2009 .

[2]  Bo Pang,et al.  A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts , 2004, ACL.

[3]  Kalapriya Kannan,et al.  Modeling the impact of review dynamics on utility value of a product , 2013, Social Network Analysis and Mining.

[4]  Pero Subasic,et al.  Affect analysis of text using fuzzy semantic typing , 2000, Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063).

[5]  P. Wilson,et al.  The Nature of Emotions , 2012 .

[6]  Lotfi A. Zadeh,et al.  The concept of a linguistic variable and its application to approximate reasoning-III , 1975, Inf. Sci..

[7]  Vincenzo Loia,et al.  A fuzzy-oriented sentic analysis to capture the human emotion in Web-based content , 2014, Knowl. Based Syst..

[8]  Bing Liu,et al.  Opinion observer: analyzing and comparing opinions on the Web , 2005, WWW '05.

[9]  John Yen,et al.  FLAME—Fuzzy Logic Adaptive Model of Emotions , 2000, Autonomous Agents and Multi-Agent Systems.

[10]  Lotfi A. Zadeh,et al.  The Concepts of a Linguistic Variable and its Application to Approximate Reasoning , 1975 .

[11]  Ilaria Tiddi,et al.  Good location, terrible food: detecting feature sentiment in user-generated reviews , 2013, Social Network Analysis and Mining.

[12]  Erik Cambria,et al.  SenticNet: A Publicly Available Semantic Resource for Opinion Mining , 2010, AAAI Fall Symposium: Commonsense Knowledge.

[13]  Ze-Jing Chuang,et al.  Multi-Modal Emotion Recognition from Speech and Text , 2004, ROCLING/IJCLCLP.

[14]  Harith Alani,et al.  Semantic Sentiment Analysis of Twitter , 2012, SEMWEB.

[15]  Francisco Herrera,et al.  Direct approach processes in group decision making using linguistic OWA operators , 1996, Fuzzy Sets Syst..

[16]  Carlo Strapparava,et al.  WordNet Affect: an Affective Extension of WordNet , 2004, LREC.

[17]  Owen Rambow,et al.  Sentiment Analysis of Twitter Data , 2011 .

[18]  Ted Pedersen,et al.  WordNet::Similarity - Measuring the Relatedness of Concepts , 2004, NAACL.

[19]  Johanna D. Moore,et al.  Twitter Sentiment Analysis: The Good the Bad and the OMG! , 2011, ICWSM.

[20]  Dipankar Das,et al.  Enhanced SenticNet with Affective Labels for Concept-Based Opinion Mining , 2013, IEEE Intelligent Systems.

[21]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[22]  Bing Liu,et al.  Sentiment Analysis and Opinion Mining , 2012, Synthesis Lectures on Human Language Technologies.

[23]  Erik Cambria,et al.  Sentic Computing: Exploitation of Common Sense for the Development of Emotion-Sensitive Systems , 2009, COST 2102 Training School.

[24]  Junlan Feng,et al.  Robust Sentiment Detection on Twitter from Biased and Noisy Data , 2010, COLING.

[25]  E. Cambria,et al.  AffectiveSpace: Blending Common Sense and Affective Knowledge to Perform Emotive Reasoning , 2009 .

[26]  Enrique Herrera-Viedma,et al.  A fuzzy linguistic model to evaluate the quality of Library 2.0 functionalities , 2013, Int. J. Inf. Manag..

[27]  F. J. Cabrerizo.  A New Quality Evaluation Model Generating Recommendations to Improve the Digital Services Provided by the Academic Digital Libraries , 2011 .

[28]  Carlo Strapparava,et al.  Learning to identify emotions in text , 2008, SAC '08.