Aspect-level sentiment analysis
It is a dataset containing sentence segments from cutomer reviews about mobile phone from different sources like Amazon, Flipkart, Tweeter and some existing datasets. It contains more than 1000 records tagged with one of the five aspect categories battery, camera, display, price and processor. Whether a sentence segment has sentiment expression (subjective/ objective) is also tagged and the sentiment orientation (positive/ negative/ neutral) of each sentence segment is assigned. Explicit or implicit presence of aspect is also maintained.
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The SaudiShopInsights dataset is a comprehensive collection of customer reviews in the Arabic language, specifically focusing on the Saudi dialect, within the domains of fashion and electronics. Gathered from various online platforms, this dataset serves as a valuable resource for researchers and practitioners interested in sentiment analysis, natural language processing, and customer behavior studies.
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Aspect Sentiment Triplet Extraction (ASTE) is an Aspect-Based Sentiment Analysis subtask (ABSA). It aims to extract aspect-opinion pairs from a sentence and identify the sentiment polarity associated with them. For instance, given the sentence ``Large rooms and great breakfast", ASTE outputs the triplet T = {(rooms, large, positive), (breakfast, great, positive)}. Although several approaches to ASBA have recently been proposed, those for Portuguese have been mostly limited to extracting only aspects without addressing ASTE tasks.
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We conduct experiments with the datasets of SemEval 2014 task4 to evaluate our model, the SemEval 2014 datasets consist of reviews in two categories: Restaurant and Laptop, and the reviews contains three labels of sentiment polarity: {positive, negative, neutral}
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