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. Research efforts are being carried out for identification of positive or negative polarity of evaluative text and for development of devices that recognize human affect, display and model emotions from textual contents. Techniques 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 being 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. 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 is being 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 or public opinion study and so on.  
In recent times, regular research papers continue to be published in reputed conferences like ACL, EMNLP or COLING. The Sentiment Analysis Symposiums are also drawing the attention of the research communities from every nook and corner of the world. There has been an increasing number of efforts in shared tasks such as , , , and relevant 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., and workshops such as , , , , , , , , in the satellite of LREC 2012, Practice and Theory of Opinion Mining and Sentiment Analysis in conjunction with , , and a bunch of special sessions like , , and so on.
Since our first workshop SAAIP 2011 in conjunction with the in Chiang Mai, Thailand during Nov. 7-13, 2011 was quite successful (with 20 submissions and more than 30 participants from many countries), we are planning to conduct our next workshop in conjunction with the to be held in Mumbai, India, during Dec. 8-15, 2012.
Inspired by the objectives we aimed at in the first edition of the workshop, the warm responses and feedbacks we received from the participants and attendees and the final outcome, the purpose of the proposed 2nd edition of the Workshop on Sentiment Analysis where AI meets Psychology (SAAIP 2012) is to create a framework for presenting and discussing the challenges related to sentiment, opinion and emotion analysis in the ground of NLP.
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. 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 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.
  • 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 specially in Social Networking
  • Multimodal Sentiment Analysis
  • Multilingual Sentiment Analysis