SAAIP 2016 : 4th Workshop on Sentiment Analysis where AI meets Psychology
Call For Papers
In recent times, research activities in the areas of Opinion, Sentiment, Emotion and/or Mood in natural language texts, speech, music and other media have become the mainstream research under the umbrella of subjectivity analysis and affective computing. These tasks are considered vital since a decade from various academic and commercial perspectives. The popularity of the Internet and the rapid expansion of social media, a variety of user generated contents become available online. However, the major challenges are how to process the user generated contents such as texts, audio and images and how to organize them in some meaningful ways.
The common interest areas where Artificial Intelligence (AI) meets sentiment analysis can be viewed from four aspects of the problem and the aspects can be grouped as 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 methodologies will be proved as the most useful ingredients from the psychologist's point of view.
Sentiment analysis from natural language texts is a multifaceted and multidisciplinary problem. Research efforts are being carried out for identification of positive or negative polarity of the evaluative text and also for the development of devices that recognize human affect, display and model emotions from textual contents. Identifying strength of sentiment in figurative texts or aspects and categories from the reviews, detecting stance from the tweet data, identifying the psychological condition of persons from chat even detecting sentiment in clinical texts and the moods from music etc. are the recent trends in the field of sentiment analysis.
Mood analysis from music is an emerging area in Music Information Retrieval (MIR). The popularity of downloading and purchasing of music from online music shops has been increased. Similarly, with rapid evolvement of technology, music is just a few clicks away, on almost any personal gadget be it computers, portable music players, or smart phones. This fact underlines the importance of developing an automated process for its organization, management, search as well as generation of playlists and various other music related applications. Recently, MIR based on emotions or moods has attracted the researchers from all over the world because of its highly motivated implications in human computer interactions.
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. Techniques from Artificial Intelligence play the important roles in these tasks.
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 or Emotion corpora, lexicon and annotation
• Applications of sentiment analysis specially on Social Network
• Music and Lyrics based Mood classification
• Playlist generation according to mood
• Personality Detection
• Temporal Extraction and Sentiment Analysis in Clinical Texts
• Multimodal Sentiment Analysis
• Multilingual Sentiment Analysis