Then for the set of aspect terms within a sentence, it was asked to determine whether the polarity of each aspect term is positive, negative, neutral or conflict. Given a set of sentences with pre-identified entities (e.g.,restaurants), the task was about identifying the aspect terms present in the sentences and returning a list containing all the distinct aspect terms. At the international level, SemEval, the most prominent evaluation campaign in the Natural Language Processing field, in 2014 SE-ABSA14 provided a benchmark dataset of reviews in English language for the ABSA task. According to the literature definition, a term/phrase is considered as an aspect when it co-occurs with some “opinion words” that indicate a sentiment polarity on it. Very often, the ABSA task is performed on a set of aspects defined a priori, limiting its applicability in the real scenario.Aspect TermExtraction(ATE) is the task of identifying an "aspect" in a text without knowing a priori the list that contains it. Aspect-based SentimentAnalysis(ABSA) is an evolution of Sentiment Analysis that aims at capturing the aspect-level opinions expressed in natural language texts. In particular, it often takes the form of an annotation task with the purpose of annotating a portion of text with a positive, negative, or neutral label. Sentiment Analysis(or Opinion Mining) is the task of identifying what the user thinks about a particular element.
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