[Elsnet-list] Call For Participation: Aspect Based Sentiment Analysis at SemEval 2014

Ion Androutsopoulos ionandr at gmail.com
Fri Dec 6 17:53:00 CET 2013

Call For Participation
SemEval 2014 Task 4 - Aspect Based Sentiment Analysis

The aim of this task is to allow a finer-grained aspect based sentiment 
analysis (ABSA). The goal is to identify the aspects (e.g. battery, 
screen; food, service) of given target entities (cf. laptop, restaurant) 
and the sentiment expressed towards each aspect.

Sentiment analysis is increasingly viewed as a vital task both from an 
academic and a commercial standpoint. The majority of existing 
evaluations in Sentiment Analysis (SA) are aimed at evaluating the 
overall polarity of a sentence, paragraph, or text span. In contrast, 
this task will provide evaluation for detecting aspects and the 
sentiment expressed towards each aspect. In particular, the task will 
consist of the following subtasks:

Subtask 1: Aspect term extraction
Given a set of sentences with pre-identified entities (e.g., 
restaurants), identify the aspect terms of the sentences. An aspect term 
names a particular aspect of the target entity (e.g., "I liked the 
service and the staff, but not the food”, “The food was nothing much, 
but I loved the staff”).

Subtask 2: Aspect term polarity
Given one or more aspect terms within a sentence, determine whether the 
polarity of each aspect term is positive, negative, neutral or conflict 
(i.e., both positive and negative). For example:
“I loved their fajitas” → “fajitas”: positive
“I hated their fajitas, but their salads were great” → “fajitas”: 
negative, “salads”: positive
“The fajitas are their first plate” → “fajitas”: neutral
“The fajitas were great to taste, but not to see” → “fajitas”: conflict

Subtask 3: Aspect category detection
Given a predefined set of aspect categories (e.g., price, food), 
identify the aspect categories discussed in a given sentence. Aspect 
categories are typically coarser than the aspect terms of Subtask 1, and 
they do not necessarily occur as terms in the given sentence. For 
example, given the set of aspect categories {food, service, price, 
ambience, anecdotes/miscellaneous}:
“The restaurant was too expensive” → {price}
“The restaurant was expensive, but the menu was great” → {price, food}

Subtask 4: Aspect category polarity
Given a set of pre-identified aspect categories (e.g., {food, price}), 
determine the polarity (positive, negative, neutral or conflict) of each 
aspect category. For example:
“The restaurant was too expensive” → {price: negative}
“The restaurant was expensive, but the menu was great” → {price: 
negative, food: positive}

Two domain-specific datasets for laptops and restaurants, consisting of 
over 6,500 sentences with fine-grained aspect-level human annotations 
will be provided for training.

Participants can participate in either all or a subset of subtasks.

Trial data ready October 31, 2013
Training data ready December 15, 2013
Evaluation period March 15-30, 2014 Paper submission due April 30, 2014 
SemEval workshop August 23-24, 2014, co-located with COLING and *SEM in 
Dublin, Ireland.

The Semeval-2014 Task 4 website includes further details on the training 
data, evaluation, and examples of expected system outputs:

Join our mailing list:
email: semeval-absa at googlegroups.com

Ion Androutsopoulos (Athens University of Economics and Business, Greece)
Dimitris Galanis (“Athena” Research Center, Greece)
Suresh Manandhar (University of York, UK) [Primary Contact]
Harris Papageorgiou ("Athena" Research Center, Greece)
John Pavlopoulos (Athens University of Economics and Business, Greece)
Maria Pontiki (“Athena” Research Center, Greece)

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