Apologies for cross-posting,

Submissions are invited for the 3rd Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM), an ICML14 workshop exploring the new frontiers of big data computing for opinion mining through machine-learning techniques and sentiment learning methods. For more information, please visit: http://sentic.net/wisdom

RATIONALE The distillation of knowledge from social media is an extremely difficult task as the content of today's Web, while perfectly suitable for human consumption, remains hardly accessible to machines. The opportunity to capture the opinions of the general public about social events, political movements, company strategies, marketing campaigns, and product preferences has raised growing interest both within the scientific community, leading to many exciting open challenges, as well as in the business world, due to the remarkable benefits to be had from marketing and financial market prediction.

Statistical NLP has been the mainstream NLP research direction since late 1990s. It relies on language models based on popular machine-learning algorithms such as maximum-likelihood, expectation maximization, conditional random fields, and support vector machines. By feeding a large training corpus of annotated texts to a machine-learning algorithm, it is possible for the system to not only learn the valence of keywords, but also to take into account the valence of other arbitrary keywords, punctuation, and word co-occurrence frequencies. However, standard statistical methods are generally semantically weak as they merely focus on lexical co-occurrence elements with little predictive value individually.

Endogenous NLP, instead, involves the use of machine-learning techniques to perform semantic analysis of a corpus by building structures that approximate concepts from a large set of documents. It does not involve prior semantic understanding of documents; instead, it relies only on the endogenous knowledge of these (rather than on external knowledge bases). The advantages of this approach over the knowledge engineering approach are effectiveness, considerable savings in terms of expert manpower, and straightforward portability to different domains. Endogenous NLP includes methods based either on lexical semantics, which focuses on the meanings of individual words (e.g., LSA, LDA, and MapReduce), or compositional semantics, which looks at the meanings of sentences and longer utterances (e.g., HMM, association rule learning, and probabilistic generative models).

TOPICS WISDOM aims to provide an international forum for researchers in the field of machine learning for opinion mining and sentiment analysis to share information on their latest investigations in social information retrieval and their applications both in academic research areas and industrial sectors. The broader context of the workshop comprehends opinion mining, social media marketing, information retrieval, and natural language processing. Topics of interest include but are not limited to: • Endogenous NLP for sentiment analysis • Sentiment learning algorithms • Semantic multi-dimensional scaling for sentiment analysis • Big social data analysis • Opinion retrieval, extraction, classification, tracking and summarization • Domain adaptation for sentiment classification • Time evolving sentiment analysis • Emotion detection • Concept-level sentiment analysis • Topic modeling for aspect-based opinion mining • Multimodal sentiment analysis • Sentiment pattern mining • Affective knowledge acquisition for sentiment analysis • Biologically-inspired opinion mining • Content-, concept-, and context-based sentiment analysis

SPEAKER Rui Xia is currently an assistant professor at School of Computer Science and Engineering, Nanjing University of Science and Technology, China. His research interests include machine learning, natural language processing, text mining and sentiment analysis. He received the Ph.D. degree from the Institute of Automation, Chinese Academy of Sciences in 2011. He has published several refereed conference papers in the areas of artificial intelligence and natural language processing, including IJCAI, AAAI, ACL, COLING, etc. He served on the program commitee member of several international conferences and workshops including IJCAI, COLING, WWW Workshop on MABSDA, KDD Workshop on WISDOM and ICDM Workshop on SENTIRE. He is a member of ACM, ACL and CCF, and he is an operating committee member of YSSNLP.

KEYNOTE One one hand, most of the existing domain adaptation studies in the field of NLP belong to the feature-based adaptation, while the research of instance-based adaptation is very scarce. One the other hand, due to the explosive growth of the Internet online reviews, we can easily collect a large amount of labeled reviews from different domains. But only some of them are beneficial for training a desired target-domain sentiment classifier. Therefore, it is important for us to identify those samples that are the most relevant to the target domain and use them as training data. To address this problem, we propose two instance-based domain adpatation methods for NLP applications. The first one is called PUIS and PUIW, which conduct instance adaptation based on instance selection and instance weighting via PU learning. The second one is called in-target-domain logistic approximation (ILA), where we conduct instance apdatation by a joint logistic approximation model. Both of methods achieve sound performance in high-dimentional NLP tasks such as cross-domain text categorization and sentiment classification.

SUBMISSIONS AND PROCEEDINGS Authors are required to follow Springer LNCS Proceedings Template and to submit their papers through EasyChair. The paper length is limited to 12 pages, including references, diagrams, and appendices, if any. As per ICML tradition, reviews are double-blind, and author names and affiliations should not be listed. Each submitted paper will be evaluated by three PC members with respect to its novelty, significance, technical soundness, presentation, and experiments. Accepted papers will be published in Springer LNCS Proceedings. Selected, expanded versions of papers presented at the workshop will be invited to a forthcoming Special Issue of Cognitive Computation on opinion mining and sentiment analysis.

TIMEFRAME • May 11th, 2014: Submission deadline • May 25th, 2014: Notification of acceptance • June 1st, 2014: Final manuscripts due • June 25th, 2014: Workshop date

ORGANIZERS • Yunqing Xia, Tsinghua University (China) • Erik Cambria, Nanyang Technological University (Singapore) • Yongzheng Zhang, LinkedIn Inc. (USA) • Newton Howard, MIT Media Laboratory (USA)