Appel: Deadline Extension, ESWC'14 Challenge on Concept-Level Sentiment Analysis

Thierry Hamon hamon at LIMSI.FR
Tue Mar 18 21:04:41 UTC 2014

Date: Sun, 16 Mar 2014 07:59:02 -0500 (EST)
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Apologies for cross-posting,

The submission deadline of the ESWC'14 Challenge on Concept-Level
Sentiment Analysis ( has been extended to
31st March. The Challenge will be held in Crete, Greece, on 25th May
2014 at the European Semantic Web Conference. The Challenge is open to
everyone from industry and academia.

Mining opinions and sentiments from natural language, however, 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.  Unlike
purely syntactical techniques, concept- based approaches are able to
detect also sentiments that are expressed in a subtle manner, e.g.,
through the analysis of concepts that do not explicitly convey any
emotion, but which are implicitly linked to other concepts that do so.

The 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 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 Challenge 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
asin 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 dataset.

Systems will be evaluated against a testing dataset which will be
revealed and released after the first-round of evaluation during the
Conference. 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. An amount of €700 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 via EasyChair:
- 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
- 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.

Please share comments and questions with the challenge mailing list. The
organizers will assist you for any potential issues that could be

- March 31, 2014, 23:59 (Hawaii time): Submission
- April 9, 2014, 23:59 (Hawaii time): Notification of acceptance
- May 27-29, 2014: Challenge days

- Erik Cambria, National University of Singapore (Singapore)
- Diego Reforgiato, CNR STLAB Laboratory (Italy)

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