Recent years have marked the beginning and expansion of the Social Web, in which people freely express and respond to opinion on a whole variety of topics. While the growing volume of subjective information available allows for better and more informed decisions of the users, the quantity of data to be analyzed imposed the development of specialized Natural Language Processing (NLP) systems that automatically detect subjectivity in text and subsequently extract, classify and summarize the opinions available on different topics. Although these research fields have been highly dynamic in the past years, dealing with subjectivity in text has proven to be a complex, interdisciplinary problem that remains far from being solved. Its challenges include the need to address the issue from different perspectives and at different levels, depending on the characteristics of the textual genre, the language(s) treated and the final application for which the analysis is done.
Inspired by the objectives we aimed at in the first edition of this workshop and the final outcome, the purpose of WASSA 2.011 is to create a framework for presenting and discussing the challenges related to subjectivity and sentiment analysis in NLP, from a theoretical and practical point of view. Moreover, taking into account that subjectivity-related phenomena have also been studied by other disciplines, such as Psychology, Philosophy, Economics, with WASSA 2.011 we would also like to open the door to an interdisciplinary dialogue on the nature, implications and applications of the topic(s) discussed. We envisage WASSA as a forum to discuss the achievements obtained so far and to analyse the different approaches to tackle the difficulties researchers are confronted with in this research area.
Download the pdf version of the CFP.
We encourage researchers to submit evaluation or position papers on topics including, but not restricted to:
- Affect, emotion, feeling, subjectivity, sentiment - concept definition and related NLP tasks;
- Resources for subjectivity and sentiment analysis;
- Subjectivity and opinion retrieval, extraction, categorization, aggregation and summarization;
- Topic and sentiment studies and applications of topic-sentiment analysis;
- Mass opinion estimation based on NLP and statistical models;
- Domain, topic and genre dependency of sentiment analysis;
- Ambiguity issues and word sense disambiguation of subjective language;
- Proposals involving the computational treatment of large amounts of data;
- Pragmatic analysis of the opinion mining task;
- Use of Semantic Web technologies for subjectivity and sentiment analysis;
- Improvement of NLP tasks using subjectivity and/or sentiment analysis;
- Adaptation of traditional tasks to the opinion scenario: opinion IR, QA, summarization;
- Intrinsic and extrinsic evaluation methodologies;
- Real-world applications of opinion mining systems.
We also encourage participants to provide demos of their systems, thus giving them the opportunity to obtain feedback on their achievements and issues. At the same time, with the help of demos, we aim at enriching the discussion forum with application-specific topics for debate.