WISDOM 2014 : 3rd Workshop on Issues of Sentiment Discovery …

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)

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WISDOM 2014 : 3rd Workshop on Issues of Sentiment Discovery …