[Humanist] 27.889 events: concept-level sentiment analysis
Humanist Discussion Group
willard.mccarty at mccarty.org.uk
Mon Mar 17 07:34:38 CET 2014
Humanist Discussion Group, Vol. 27, No. 889.
Department of Digital Humanities, King's College London
Submit to: humanist at lists.digitalhumanities.org
Date: Sun, 16 Mar 2014 12:59:02 +0000
From: feeds <feeds at sentic.net>
Subject: Deadline Extension: ESWC'14 Challenge on Concept-Level Sentiment Analysis
The submission deadline of the ESWC'14 Challenge on Concept-Level Sentiment
Analysis (http://sentic.net/challenge) 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
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 http://sentic.net/challenge/sentence.
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
asinhttp://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.
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 dataset.
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
- 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. 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.
HOW TO PARTICIPATE
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 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.
Please share comments and questions with the challenge mailing list. The
organizers will assist you for any potential issues that could be raised.
- 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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