Appel: ESWC 2014, Challenge Concept-Level Sentiment Analysis

Thierry Hamon hamon at LIMSI.FR
Tue Feb 11 21:03:16 UTC 2014

Date: Mon, 10 Feb 2014 15:55:04 +0100 (CET)
From: speroni at
Message-Id: <20140210145524.577BFDBA76 at>

** apologies for cross-posting **

==== Second Call for Challenge: Concept-Level Sentiment Analysis ====
Challenge Website:
Call Web page:

11th Extended Semantic Web Conference (ESWC) 2014
Dates: May 25 - 29, 2014
Venue: Anissaras, Crete, Greece
Hashtag: #eswc2014
Feed: @eswc_conf
General Chair: Valentina Presutti (STLab, ISTC-CNR, IT)
Challenge Coordinator: Milan Stankovic (Sepage & Universite
Paris-Sorbonne, FR)
Challenge Chairs:
- Erik Cambria (National University of Singapore, SG)
- Diego Reforgiato (STLab, ISTC-CNR, IT)


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).


The Challenge is open to everyone from industry and academia.


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

* 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 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 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 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 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


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


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.


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
* 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. Winners will be selected only
for tasks with at least 3 participants. In any case all submissions will
be reviewed and, if accepted, published in ESWC post-proceedings. An
amount of 700 euros has already been secured for the first task for what
the first point of the evaluation aspects is concerned. We are currently
working on securing further funding.


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
  (, 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:


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


* March 14, 2014, 23:59 CET: Submission due
* April 9, 2014, 23:59 CET: Notification of acceptance
* May 27-29, 2014: The Challenge takes place at ESWC-14


* Newton Howard, MIT Media Laboratory (USA)
* Cheng Xiang Zhai, University of Illinois at Urbana-Champaign (USA)
* Rada Mihalcea, University of North Texas (USA)
* Ping Chen, University of Houston-Downtown (USA)
* Yongzheng Zhang, LinkedIn Inc. (USA)
* Giuseppe Di Fabbrizio, Amazon Inc. (USA)
* Rui Xia, Nanjing University of Science and Technology (China)
* Rafal Rzepka, Hokkaido University (Japan)
* Amir Hussain, University of Stirling (UK)
* Alexander Gelbukh, National Polytechnic Institute (Mexico)
* Bjoern Schuller, Technical University of Munich (Germany)
* Amitava Das, Samsung Research India (India)
* Dipankar Das, National Institute of Technology (India)
* Carlo Strapparava, Fondazione Bruno Kessler (Italy)
* Stefano Squartini, Marche Polytechnic University (Italy)
* Cristina Bosco, University of Torino (Italy)
* Paolo Rosso, Technical University of Valencia (Spain)

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