M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is a crucial task in information extraction and sentiment analysis, aiming to identify aspects with associated sentiment elements in text. However, existing ABSA datasets are predominantly English-centric, limiting the scope for multilingual evaluation and research. To bridge this gap, we present M-ABSA, a comprehensive dataset spanning 7 domains and 21 languages, making it the most extensive multilingual parallel dataset for ABSA to date. Our primary focus is on triplet extraction, which involves identifying aspect terms, aspect categories, and sentiment polarities. The dataset is constructed through an automatic translation process with human review to ensure quality. We perform extensive experiments using various baselines to assess performance and compatibility on M-ABSA. Our empirical findings highlight that the dataset enables diverse evaluation tasks, such as multilingual and multidomain transfer learning, and large language model evaluation, underscoring its inclusivity and its potential to drive advancements in multilingual ABSA research. 1 annotating a subset of sentences. We select 2,147 annotated sentences from this subset. Food. This dataset includes approximately 500K Amazon fine food reviews curated from a Kaggle competition. Chebolu et al. (2024a) leveraged this dataset for the OATS task. We select 2,136 annotated sentences from this collection. Coursera. This dataset contains around 100K reviews from Coursera, focusing on course quality, content, comprehensiveness, etc, sourced from a Kaggle competition. Chebolu et al. (2024a) also employed this dataset for the OATS task, from which we select 2,156 annotated sentences. Phone. This dataset, created by Zhou et al. ( 2023 ), focuses on the OATS task. It includes reviews from various e-commerce platforms, collected in mid-2021, covering 12 cellphone brands. We select a subset of 2,109 sentences. Laptop. This dataset features reviews from the Amazon platform between 2017 and 2018, covering ten types of laptops across six brands (Cai et al., 2021). We select 2,122 sentences out of 4,076 review sentences. Restaurant. This dataset contains customer reviews for restaurants. It is one of the domain datasets from the SemEval 2016 (Pontiki et al., 2016) and is constructed for TASD tasks with the aspect-based sentiment triplets. Although the dataset provides reviews for 5 languages including English, reviews are not parallel. We select 2,124 reviews from English to create our parallel dataset.