Appel: ESWC 2014, Call for Challenge, Concept-Level Sentiment Analysis
Thierry Hamon
hamon at LIMSI.FR
Wed Dec 4 13:10:55 UTC 2013
Date: Tue, 3 Dec 2013 12:17:24 +0100 (CET)
From: speroni at cs.unibo.it
Message-Id: <20131203111744.59CD6DBB48 at vina.cines.fr>
X-url: http://challenges.2014.eswc-conferences.org/SemSA
==== Call for Challenge: Concept-Level Sentiment Analysis ====
Challenge Website: http://challenges.2014.eswc-conferences.org/SemSA
Call Web page: http://2014.eswc-conferences.org/important-dates/call-SemSA
MOTIVATION AND OBJECTIVES
Mining opinions and sentiments from natural language is an extremely
difficult task as it involves a deep understanding of most of the
explicit and implicit, regular and irregular, syntactical and semantic
rules proper of a language. Existing approaches mainly rely on parts of
text in which opinions and sentiments are explicitly expressed such as
polarity terms, affect words and their co-occurrence
frequencies. However, opinions and sentiments are often conveyed
implicitly through latent semantics, which make purely syntactical
approaches ineffective. To this end, concept-level sentiment analysis
aims to go beyond a mere word-level analysis of text and provide novel
approaches to opinion mining and sentiment analysis that allow a more
efficient passage from (unstructured) textual information to
(structured) machine-processable data, in potentially any domain.
Concept-level sentiment analysis focuses on a semantic analysis of text
through the use of web ontologies or semantic networks, which allow the
aggregation of conceptual and affective information associated with
natural language opinions. By relying on large semantic knowledge bases,
concept-level sentiment analysis steps away from blind use of keywords
and word co-occurrence count, but rather relies on the implicit features
associated with natural language concepts.
This Challenge focuses on the introduction, presentation, and discussion
of novel approaches to concept-level sentiment analysis. Participants
will have to design a concept-level opinion-mining engine that exploits
common-sense knowledge bases, e.g., SenticNet, and/or Linked Data and
Semantic Web ontologies, e.g., DBPedia, to perform multi-domain
sentiment analysis. The main motivation for the Challenge, in
particular, is to go beyond a mere word-level analysis of natural
language text and provide novel concept-level tools and techniques that
allow a more efficient passage from (unstructured) natural language to
(structured) machine-processable data, in potentially any domain.
Systems must have a semantics flavor (e.g., by making use of Linked Data
or known semantic networks within their core functionalities) and
authors need to show how the introduction of semantics can be used to
obtain valuable information, functionality or performance. Existing
natural language processing methods or statistical approaches can be
used too as long as the semantics plays a main role within the core
approach (engines based merely on syntax/word-count will be excluded
from the competition).
TARGET AUDIENCE
The Challenge is open to everyone from industry and academia.
TASKS
The Concept-Level Sentiment Analysis Challenge is defined in terms of
different tasks. The first task is elementary whereas the others are
more advanced. The input units of each task are sentences. Sentences are
assumed to be in grammatically correct American English and have to be
processed according to the input format specified at
http://sentic.net/challenge/sentence.
Elementary Task: Polarity Detection
The main goal of the task is polarity detection. The proposed systems
will be assessed according to precision, recall and F-measure of
detected binary polarity values (1=positive; 0=negative) for each input
sentence of the evaluation dataset, following the same format as in
http://sentic.net/challenge/task0. The problem of subjectivity detection
is not addressed within this Challenge, hence participants can assume
that there will be no neutral sentences. Participants are encouraged to
use the Sentic API or further develop and apply sentic computing tools.
Advanced Task #1: Aspect-Based Sentiment Analysis
The output of this task will be a set of aspects of the reviewed product
and a binary polarity value associated to each of such aspects, in the
format specified at http://sentic.net/challenge/task1. So, for example,
while for the Elementary task an overall polarity (positive or negative)
is expected for a review about a mobile phone, this task requires a set
of aspects (such as ‘speaker', ‘touchscreen', ‘camera', etc.) and a
polarity value (positive OR negative) associated with each of such
aspects. Systems will be assessed according to both aspect extraction
and aspect polarity detection.
Advanced Task #2: Semantic Parsing
As suggested by the title, the Challenge focuses on sentiment analysis
at concept-level. This means that the proposed systems are not supposed
to work at word/syntax level but rather work with
concepts/semantics. Hence, this task will evaluate the capability of the
proposed systems to deconstruct natural language text into concepts,
following the same format as in
http://sentic.net/challenge/task2. SenticNet will be taken as a
reference to test the efficiency of the proposed parsers, but extracted
concepts won't necessary have to match SenticNet concepts. The proposed
systems, for example, are supposed to be able to extract a multi-word
expression like ‘buy christmas present' from sentences such as “Today I
bought a lot of very nice Christmas presents'. The number of extracted
concepts per sentence will be assessed through precision, recall and
F-measure against the evaluation dataset.
Advanced Task #3: Topic Spotting
Input sentences will be about four different domains, namely: books,
DVDs, electronics, and kitchen appliances. This task focuses on the
automatic classification of sentences into one of such domains, in the
format specified at http://sentic.net/challenge/task3. All sentences are
assumed to belong to only one of the above-mentioned domains. The
proposed systems are supposed to exploit the extracted concepts to infer
which domain each sentence belongs to. Classification accuracy will be
evaluated in terms of precision, recall and F-measure against the
evaluation dataset.
EVALUATION DATASET
Systems will be evaluated against a testing dataset which will be
revealed and released after the first-round of evaluation during the
Conference. The dataset will be made public on the challenge
website. Participants are suggested to train and/or test their own
systems using the Blitzer Dataset. The testing dataset will be
constructed in the same way and from the same sources as the Blitzer
dataset.
EVALUATION
The evaluation will be performed by the members of the Program
Committee. For systems that can be tuned with different parameters,
please indicate a range of up to 4 sets of settings. Settings with the
best F-measures will be considered for judgment. For each system,
reviewers will give a numerical score within the range [1-10] and
details motivating their choice. The scores will be given to the
following aspects:
1. Use of common-sense knowledge and semantics;
2. Precision, recall, and F-measure wrt the selected task;
3. Computational time;
4. Innovative nature of the approach.
JUDGING AND PRIZES
After a first round of review, the Program Committee and the chairs will
select a number of submissions confirming to the challenge requirements
that will be invited to present their work. Submissions accepted for
presentation will be included in post-proceedings and will receive
constructive reviews from the Program Committee. All accepted
submissions will have a slot in a poster session dedicated to the
challenge. In addition, the winners will present their work in a special
slot of the main program of ESWC and will be invited to submit a paper
to a dedicated Semantic Web Journal special issue.
For the Concept-Level Sentiment Analysis Challenge there will be two
awards for each task:
* Quantitative: the system with the highest average score in items 1-3
above;
* Innovative: the system with the highest score in item 4 above.
There will be a board of judges at the conference who will evaluate
again the systems in more detail. The judges will then meet in private
to discuss the entries and to determine the winners. It may happen that
the same system runs for both the awards.
HOW TO PARTICIPATE
The following information has to be provided:
* Abstract: no more than 200 words.
* Description: It should contain the details of the system, including
why the system is innovative, how it uses Semantic Web, which features
or functions the system provides, what design choices were made and what
lessons were learned. The description should also summarize how
participants have addressed the evaluation tasks. Papers must be
submitted in PDF format, following the style of the Springer's Lecture
Notes in Computer Science (LNCS) series
(http://www.springer.com/computer/lncs/lncs+authors), and not exceeding
5 pages in length.
* Web Access: The application can either be accessible via the web or
downloadable. If the application is not publicly accessible, password
must be provided. A short set of instructions on how to use the
application should be provided as well.
All submissions should be provided via EasyChair
https://www.easychair.org/conferences/?conf=eswc2014-challenges
Please share comments and questions with the challenge mailing list. The
organizers will assist you for any potential issues that could be
raised.
MAILING LIST
We invite the potential participants to subscribe to our mailing list in
order to be kept up to date with the latest news related to the
challenge.
https://lists.sti2.org/mailman/listinfo/eswc2014-semsa-challenge
IMPORTANT DATES
* March 7, 2014, 23:59 (Hawaii time): Abstract Submission
* March 14, 2014, 23:59 (Hawaii time): Submission
* April 9, 2014, 23:59 (Hawaii time): Notification of acceptance
* May 27-29, 2014: Challenge days
CHALLENGE CHAIRS
* Erik Cambria (National University of Singapore, SG)
* Diego Reforgiato Recupero (CNR STLAB Laboratory, IT)
PROGRAM COMMITTEE
* Newton Howard (MIT Media Laboratory, US)
* ChengXiang Zhai (University of Illinois at Urbana-Champaign, US)
* Rada Mihalcea (University of North Texas, US)
* Ping Chen (University of Houston-Downtown, US)
* Yongzheng Zhang (LinkedIn Inc., US)
* Giuseppe Di Fabbrizio (Amazon Inc., US)
* Rui Xia (Nanjing University of Science and Technology, CN)
* Rafal Rzepka (Hokkaido University, JP)
* Amir Hussain (University of Stirling, UK)
* Alexander Gelbukh (National Polytechnic Institute, MX)
* Bjoern Schuller, (Technical University of Munich, DE)
* Amitava Das (Samsung Research India, IN)
* Dipankar Das (National Institute of Technology, IN)
* Carlo Strapparava (Fondazione Bruno Kessler, IT)
* Stefano Squartini (Marche Polytechnic University, IT)
* Cristina Bosco (University of Torino, IT)
* Paolo Rosso (Technical University of Valencia, ES)
ESWC CHALLENGE COORDINATOR
* Milan Stankovic (Sepage & Universite Paris-Sorbonne, FR)
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