Affective Computing and Sentiment Analysis

Understanding emotions is an important aspect of personal development and growth, and as such it is a key tile for the emulation of human intelligence. Besides being important for the advancement of AI, emotion processing is also important for the closely related task of polarity detection. The opportunity to automatically capture the general public's sentiments about social events, political movements, marketing campaigns, and product preferences has raised interest in both the scientific community, for the exciting open challenges, and the business world, for the remarkable fallouts in marketing and financial market prediction. This has led to the emerging fields of affective computing and sentiment analysis, which leverage human-computer interaction, information retrieval, and multimodal signal processing for distilling people's sentiments from the ever-growing amount of online social data.

[1]  The Future of the Social Web, Papers from the 2011 ICWSM Workshop, Barcelona, Catalonia, Spain, July 21, 2011 , 2011, The Future of the Social Web.

[2]  Zhihong Zeng,et al.  A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Swapna Somasundaran,et al.  Discourse Level Opinion Interpretation , 2008, COLING.

[4]  Andrea Esuli,et al.  SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining , 2006, LREC.

[5]  J. Mikels,et al.  Characterization of the Affective Norms for English Words by discrete emotional categories , 2007, Behavior research methods.

[6]  Erik Cambria,et al.  Label Embedding for Zero-shot Fine-grained Named Entity Typing , 2016, COLING.

[7]  Navneet Kaur,et al.  Opinion mining and sentiment analysis , 2016, 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom).

[8]  Yoshua Bengio,et al.  Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach , 2011, ICML.

[9]  Erik Cambria,et al.  Common Sense Knowledge Based Personality Recognition from Text , 2013, MICAI.

[10]  Theresa Wilson,et al.  Multimodal Subjectivity Analysis of Multiparty Conversation , 2008, EMNLP.

[11]  Erik Cambria,et al.  Convolutional MKL Based Multimodal Emotion Recognition and Sentiment Analysis , 2016, 2016 IEEE 16th International Conference on Data Mining (ICDM).

[12]  David Vilares,et al.  Lyapunov filtering of objectivity for Spanish Sentiment Model , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[13]  Yücel Saygin,et al.  SU-Sentilab : A Classification System for Sentiment Analysis in Twitter , 2013, *SEMEVAL.

[14]  Erik Cambria,et al.  Fusing audio, visual and textual clues for sentiment analysis from multimodal content , 2016, Neurocomputing.

[15]  Erik Cambria,et al.  Jumping NLP Curves: A Review of Natural Language Processing Research [Review Article] , 2014, IEEE Computational Intelligence Magazine.

[16]  Felipe Bravo-Marquez,et al.  Meta-level sentiment models for big social data analysis , 2014, Knowl. Based Syst..

[17]  David E. Losada,et al.  An empirical study of sentence features for subjectivity and polarity classification , 2014, Inf. Sci..

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

[19]  Diego Reforgiato Recupero,et al.  Sentilo: Frame-Based Sentiment Analysis , 2014, Cognitive Computation.

[20]  Erik Cambria,et al.  A graph-based approach to commonsense concept extraction and semantic similarity detection , 2013, WWW.

[21]  Björn W. Schuller,et al.  SenticNet 4: A Semantic Resource for Sentiment Analysis Based on Conceptual Primitives , 2016, COLING.

[22]  Björn W. Schuller,et al.  New Avenues in Opinion Mining and Sentiment Analysis , 2013, IEEE Intelligent Systems.

[23]  Erik Cambria,et al.  Word Polarity Disambiguation Using Bayesian Model and Opinion-Level Features , 2014, Cognitive Computation.

[24]  Christopher Potts,et al.  Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank , 2013, EMNLP.

[25]  M. Minsky The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind , 2006 .

[26]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[27]  Raymond Y. K. Lau,et al.  A Probabilistic Generative Model for Mining Cybercriminal Networks from Online Social Media , 2014, IEEE Computational Intelligence Magazine.

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

[29]  Andrew Ortony,et al.  The Cognitive Structure of Emotions , 1988 .

[30]  Rada Mihalcea,et al.  Towards multimodal sentiment analysis: harvesting opinions from the web , 2011, ICMI '11.

[31]  Erik Cambria,et al.  A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks , 2016, COLING.

[32]  Erik Cambria,et al.  Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis , 2015 .

[33]  Rafael A. Calvo,et al.  Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications , 2010, IEEE Transactions on Affective Computing.

[34]  Nicu Sebe,et al.  Affective multimodal human-computer interaction , 2005, ACM Multimedia.

[35]  Erik Cambria,et al.  Aspect extraction for opinion mining with a deep convolutional neural network , 2016, Knowl. Based Syst..

[36]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.

[37]  Erik Cambria,et al.  SenticNet 3: A Common and Common-Sense Knowledge Base for Cognition-Driven Sentiment Analysis , 2014, AAAI.

[38]  Erik Cambria,et al.  The CLSA Model: A Novel Framework for Concept-Level Sentiment Analysis , 2015, CICLing.

[39]  Mauro Dragoni,et al.  A Fuzzy System for Concept-Level Sentiment Analysis , 2014, SemWebEval@ESWC.

[40]  Erik Cambria,et al.  Sentiment Data Flow Analysis by Means of Dynamic Linguistic Patterns , 2015, IEEE Computational Intelligence Magazine.

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

[42]  Björn W. Schuller,et al.  Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge , 2011, Speech Commun..

[43]  Lei Zhang,et al.  Sentiment Analysis and Opinion Mining , 2017, Encyclopedia of Machine Learning and Data Mining.

[44]  Davide Anguita,et al.  Statistical Learning Theory and ELM for Big Social Data Analysis , 2016, IEEE Computational Intelligence Magazine.

[45]  Richard Tzong-Han Tsai,et al.  Improve Polarity Detection of Online Reviews with Bag-of-Sentimental-Concepts , 2014 .

[46]  Haixun Wang,et al.  Guest Editorial: Big Social Data Analysis , 2014, Knowl. Based Syst..

[47]  Fabrício Benevenuto,et al.  iFeel: a system that compares and combines sentiment analysis methods , 2014, WWW.

[48]  Tejashri Inadarchand Jain,et al.  Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis , 2010 .

[49]  Amit Konar,et al.  Emotion Recognition: A Pattern Analysis Approach , 2015 .

[50]  Rosalind W. Picard Affective Computing , 1997 .

[51]  Delip Rao,et al.  Semi-Supervised Polarity Lexicon Induction , 2009, EACL.

[52]  Rada Mihalcea,et al.  What Men Say, What Women Hear: Finding Gender-Specific Meaning Shades , 2016, IEEE Intelligent Systems.

[53]  Björn W. Schuller,et al.  Categorical and dimensional affect analysis in continuous input: Current trends and future directions , 2013, Image Vis. Comput..