In recent times, research activities in the areas of Opinion, Sentiment and/or Emotion in natural language texts and other media are gaining ground under the umbrella of subjectivity analysis and affect computing. The reason may be the huge amount of available text data in the Social Web in the forms of news, reviews, blogs, chats and even twitter. Though Sentiment analysis from natural language text is a multifaceted and multidisciplinary problem, in general, the term “sentiment” is used in reference to the automatic analysis of evaluative text. Not only the identification of positive or negative polarity of such evaluative text, research to develop devices that recognize human affect, display and model emotions from textual contents are also being carried out. Techniques and methodologies from Artificial Intelligence play important roles in these tasks. 
The main four aspects of the sentiment analysis problem are Object identification, Feature extraction, Orientation classification and Integration. The existing reported solutions or available systems are still far from perfect or fail to meet the satisfaction level of the end users. The main issue may be that there are many conceptual rules that govern sentiment and there are even more clues (possibly unlimited) that can convey these concepts from realization to verbalization of a human being. Human psychology may provide the unrevealed clues and govern the sentiment realization. Human psychology relates to social, cultural, behavioral and environmental aspects of civilization.
In the present scenario we need constant research endeavors to reveal and incorporate the human psychological knowledge into machines in the best possible ways. The important issues that need attention include how various psychological phenomena can be explained in computational terms and which AI concepts and computer modeling methodologies will prove most useful from the psychologist's point of view. 
In addition to Question Answering or Information Retrieval systems, Topic-sentiment analysis can be applied as a new research method for mass opinion estimation (e.g., reliability, validity, sample bias), psychiatric treatment, corporate reputation measurement, political orientation categorization, stock market prediction, customer preference study, public opinion study and so on. 
In recent times, regular research papers continue to be published in reputed conferences like ACL, EMNLP or COLING. There has been an increasing number of efforts in shared tasks such as SemEval 2007 Task#14: Affective Text, TAC 2008 Opinion Summarization task, TREC-BLOG tracks since 2006 and relevant NTCIR tracks since 6th NTCIR aimed to focus on different issues of opinion and emotion analysis. Several communities from sentiment analysis have engaged themselves to conduct relevant conferences, e.g., Affective Computing and Intelligent Interfaces (ACII) in 2009 and 2011 and workshops such as “Sentiment and Subjectivity in Text” in COLING-ACL 2006, “Sentiment Analysis – Emotion, Metaphor, Ontology and Terminology (EMOT)” in LREC 2008, Opinion Mining and Sentiment Analysis (WOMSA) 2009, “Topic-Sentiment Analysis for Mass Opinion Measurement (TSA)” in CIKM 2009, “Computational Approaches to Analysis and Generation of Emotion in Text” in NAACL 2010, Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA) in ECAI 2010 and in ACL 2011, FLAIRS 2011 special track on “Affect Computing” and so on.
This workshop aims to bring together the researchers in multiple disciplines such as computer science, psychology, cognitive science, social science and many more who are interested in developing next generation machines that can recognize and respond to the sentimental states of the human users and serve the society. The workshop will consist of a set of invited talks and presentations of technical papers that will be selected after peer review from the submissions received.

List of Topics

We welcome original and unpublished submissions on all aspects of sentiment analysis. Topics of interest include, but are not limited to:

  • New models of sentiment: its origin in the speaker's goals and intentions, its
    signaling in the text, and its relationships to the objects in question
  • Psychological models for sentiment analysis
  • Topic-dependent/independent sentiment identification/ Topic and sentiment studies
    and applications
  • Mass opinion estimation based on NLP and statistical models. 
  • Domain, topic and genre, language  dependency of sentiment analysis 
  • Discourse analysis of sentiment
  • Opinion, Sentiment, Emotion extraction, categorization and aggregation
  • Sentiment corpora and annotation
  • Sentiment lexicon
  • Evaluation methodologies
  • Applications of sentiment analysis