[SenticNet] CFP: ICML14 Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM)

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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)