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

Tue Apr 15 14:50:38 UTC 2014

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 fortraining 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)

Yunqing Xia, Dr.

Associate Professor
Center for Speech and Language Technologies
Tsinghua University

http://sentic.net/wisdom
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