[Corpora-List] Call for Participation TASS at SEPLN 2014: Sentiment Analysis at SEPLN

Eugenio Martínez Cámara emcamara at ujaen.es
Thu May 8 15:45:50 UTC 2014


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SEPLN 2014 workshop on:

Sentiment Analysis at SEPLN
(TASS 2014)


Sep 16th, 2014
Gerona, Spain

http://www.daedalus.es/TASS


Registration:  http://www.daedalus.es/TASS2014/tass2014.php#contact


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TASS is an experimental evaluation workshop for sentiment analysis and
online reputation analysis focused on Spanish language, organized as a
satellite event of the annual SEPLN Conference. After two successful
editions in 2012 and 2013, TASS 2014 will be held on September 16th, 2014
at Universitat de Girona, Spain.

Currently market research using user surveys is typically performed.
However, the rise of social media such as blogs and social networks and the
increasing amount of user-generated contents in the form of reviews,
recommendations, ratings and any other form of opinion, has led to creation
of an emerging trend towards online reputation analysis. This analysis has
two technological aspects: sentiment analysis and text classification (or
categorization).

First, the so-called sentiment analysis, i.e., the application of natural
language processing and text analytics to identify and extract subjective
information from texts, which is the first step towards the online
reputation analysis, is becoming a promising topic in the field of
marketing and customer relationship management, as the social media and its
associated word-of-mouth effect is turning out to be the most important
source of information for companies and their customers' sentiments towards
their brands and products.

Then, automatic text classification is used to guess the topic of the text,
among those of a predefined set of categories or classes, so as to be able
to assign the reputation level of the company into different facets, axis
or points of view of analysis.

Sentiment analysis is a major technological challenge. The task is so hard
that even humans often disagree on the sentiment of a given text. The fact
that issues that one individual finds acceptable or relevant may not be the
same to others, along with multilingual aspects, cultural factors and
different contexts make it very hard to classify a text written in a
natural language into a positive or negative sentiment. And the shorter the
text is, for example, when analyzing Twitter messages or short comments in
Facebook, the harder the task becomes.

On the other hand, text classification techniques, although studied for a
longer time, still need more research effort to be able to build complex
models with many categories with less workload and increase the precision
and recall of the results. In addition, these models should work well with
short texts and deal with specific text features that are present in social
media messages (such as spelling mistakes, abbreviations, SMS language,
etc.).

Within this context, the aim of TASS is to provide a forum for discussion
and communication where the latest research work and developments in the
field of sentiment analysis in social media, specifically focused on
Spanish language, can be shown and discussed by scientific and business
communities. The main objective is to promote the application of existing
state-of-the-art algorithms and techniques and the design of new ones for
the implementation of complex systems able to perform a sentiment analysis
and text classification on short text opinions extracted from social media
messages (specifically Twitter) published by a series of representative
personalities.

The challenge task is intended to provide a benchmark forum for comparing
the latest approaches in these fields. In addition, with the creation and
release of the fully tagged corpus, we aim to provide a benchmark dataset
that enables researchers to compare their algorithms and systems.

Four tasks are proposed for the participants covering different aspects of
sentiment analysis and automatic text classification.

*** Task 1: Sentiment Analysis at global level ***

This task consists on performing an automatic sentiment analysis to
determine the global polarity (using 5 levels) of each message in the test
set of the General corpus (see below). This task is a reedition of the task
in the previous years. Participants will be provided with the training set
of the General corpus so that they may train and validate their models.

*** Task 2: Topic classification ***

The challenge is to build a classifier to automatically identify the topic
of each message in the test set of the General corpus. Again, a reedition
of the same task in previous years. Participants may use the training set
of the General corpus to train and validate their models.

*** Task 3: Aspect detection ***
The objective is the automatic identification of the different aspects
expressed by users, among a predefined list, in their opinions in Twitter
about a given topic. A new Social-TV corpus will be used for the training
and evaluation of the systems.

*** Task 4: Aspect-based sentiment analysis ***
Systems in this task must identify the polarity of the aspect that was
detected in the previous task. Again. participants will be provided with
the Social-TV corpus to train and evaluate their models. This task is
equivalent to Task 1 but focused on fine-grained polarity detection.


Organizers

==========

Julio Villena-Román                                     Daedalus, Spain
Janine García-Morera                                    Daedalus, Spain
José Carlos González-Cristóbal Technical University of Madrid, Spain
(GSI-UPM)
L. Alfonso Ureña-López University of Jaén, Spain (SINAI-UJAEN)
Miguel Ángel García-Cumbreras University of Jaén, Spain (SINAI-UJAEN)
María-Teresa Martín-Valdivia University of Jaén, Spain (SINAI-UJAEN)
Eugenio Martínez-Cámara University of Jaén, Spain (SINAI-UJAEN)

Program Committee (to be confirmed)

=================

Alexandra Balahur EC-Joint Research Centre, Italy
José Carlos Cortizo European University of Madrid, Spain
Ana García-Serrano UNED, Spain
José María Gómez-Hidalgo Optenet, Spain
Julio Gonzalo-Arroyo UNED, Spain
Carlos A. Iglesias-Fernández Technical University of Madrid, Spain
Zornitsa Kozareva Information Sciences Institute, USA
Sara Lana-Serrano Technical University of Madrid, Spain
Paloma Martínez-Fernandez Carlos III University of Madrid, Spain
Ruslan Mitkov University of Wolverhampton, U.K.
Andrés Montoyo University of Alicante, Spain
Rafael Muñoz University of Alicante, Spain
Constantine Orasan University of Wolverhampton, U.K.S
Paolo Rosso Technical University of Valencia, Spain
Mike Thelwall University of Wolverhampton, U.K.
José Antonio Troyano University of Seville, Spain


Important Dates

=================

May 5th, 2014: Release of tasks and General corpus.
May 26th, 2014: Release of Social-TV corpus.
June 29th, 2014: Experiment submissions by participants.
July 1st, 2014: Evaluation results.
July 13th, 2014: Submission of papers.
September 16th, 2014: Workshop.


Contact Address

===============

tass at daedalus.es

http://www.daedalus.es/T2014/tass2014.php#contact


===============


Eugenio Martínez Cámara.
Grupo de Investigación SINAI <http://sinai.ujaen.es> /
SINAI<http://sinai.ujaen.es/> Research
Group.
Departamento de Informática / Computer Science Department.
Universidad de Jaén / University of Jaén.
emcamara at ujaen dot es
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