Appel: SenticNet, ICML14 workshop on sentiment analysis

Thierry Hamon hamon at LIMSI.FR
Wed Mar 26 20:45:14 UTC 2014

Date: Mon, 24 Mar 2014 04:49:50 -0500 (EST)
From: feeds <feeds at>
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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:

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).

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

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

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

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