[open-linguistics] [SenticNet] CFP: ICML14 workshop on sentiment analysis

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Apologies for cross-posting,

Submissions are invited for MASALA (Machine-learning Approaches to Sentiment
Analysis and Learning Algorithms), 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/masala

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 if 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
MASALA 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
• Big social data analysis
• Opinion retrieval, extraction, classification, tracking and summarization
• Domain specific sentiment analysis and model adaptation
• Emotion detection
• Sentiment pattern mining
• Concept-level sentiment analysis
• Biologically-inspired opinion mining
• Social-network motivated methods for natural language processing
• Topic modeling for aspect-based sentiment analysis
• Learning to rank for social media
• Content-based and social-based recommendation
• Multimodal sentiment analysis
• Content-, concept-, and context-based sentiment analysis

TIMEFRAME
• April 20th, 2014: Submission deadline
• May 11th, 2014: Notification of acceptance
• May 18th, 2014: Final manuscripts due
• June 25th, 2014: Workshop date

ORGANIZERS
• Yunqing Xia, Tsinghua University (China)
• Erik Cambria, National University of Singapore (Singapore)
• Newton Howard, MIT Media Laboratory (USA)