Abstract
Aspect-Based Sentiment Analysis (ABSA) task is one of the Natural Language Processing (NLP) research fields that has seen considerable scientific advancements over the last few years. This task aims to detect, in a given text, the sentiment of users towards the different aspects of a product or service. Despite the big number of annotated corpora that have been produced to perform the ABSA task in the English language, resources are still stingy, for other languages. Due to the lack of French corpora created for the ABSA task, we present in this paper the French corpus for the mobile phone domain “FreMPhone”. The constructed corpus consists of 5217 mobile phone reviews collected from the Amazon.fr website. Each review in the corpus was annotated with its appropriate aspect terms and sentiment polarity, using an annotation guideline. The FreMPhone contains 19257 aspect terms divided into 13259 positives, 5084 negatives, and 914 neutrals. Moreover, we proposed a new architecture “CBCF” that combines the deep learning models (LSTM and CNN) and the machine learning model CRF and we evaluated it on the FreMPhone corpus. The experiments were performed on the subtasks of the ABSA: Aspect Extraction (AE) and Sentiment Classification (SC). These experiments showed the good performance of the CBCF architecture which overrode the LSTM, CNN, and CRF models and achieved an F-measure value equal to 95.96% for the Aspect Extraction (AE) task and 96.35% for the Sentiment Classification (SC) task.
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Hammi, S., Hammami, S.M., Belguith, L.H. (2023). FreMPhone: A French Mobile Phone Corpus for Aspect-Based Sentiment Analysis. In: Nguyen, N.T., et al. Advances in Computational Collective Intelligence. ICCCI 2023. Communications in Computer and Information Science, vol 1864. Springer, Cham. https://doi.org/10.1007/978-3-031-41774-0_19
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DOI: https://doi.org/10.1007/978-3-031-41774-0_19
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