Appel: ICML14 Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM)

[Thierry Hamon]

Date: Tue, 15 Apr 2014 07:09:40 -0500 (EST)
From: feeds <feeds at sentic.net>
Message-ID: <87965822.1259606.1397563780748.open-xchange at bosoxweb03.eigbox.net>
X-url: http://sentic.net/wisdom

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)