Two New Large Corpora for Vietnamese Aspect-based Sentiment Analysis at Sentence Level

Abstract

Aspect-based sentiment analysis has been studied in both research and industrial communities over recent years. For the low-resource languages, the standard benchmark corpora play an important role in the development of methods. In this article, we introduce two benchmark corpora with the largest sizes at sentence-level for two tasks: Aspect Category Detection and Aspect Polarity Classification in Vietnamese. Our corpora are annotated with high inter-annotator agreements for the restaurant and hotel domains. The release of our corpora would push forward the low-resource language processing community. In addition, we deploy and compare the effectiveness of supervised learning methods with a single and multi-task approach based on deep learning architectures. Experimental results on our corpora show that the multi-task approach based on BERT architecture outperforms the neural network architectures and the single approach. Our corpora and source code are published on this footnoted site. 1

Sentiment Analysis in Low-Resource Settings: A Comprehensive Review of Approaches, Languages, and Data Sources

“…[99], [101], [102], [103], [108], [91], [94], [90], [101], [64] N/A [47], [48], [51], [94], [88], [106] The evaluation metric of low-resources sentiment, analysis encompasses accuracy, recall, precision, and F1 score. The best-performing metrics are the only ones reported in the SLR.…”

“…In an approach of transfer learning by [88] explored two tasks and two benchmark corpora with large sizes in Vietnamese aspect polarity classification and aspect category detection. They use deep learning architectures to deploy supervised learning techniques for different task classifications and compare the efficacy of their manual annotation.…”

“…Nonetheless, challenges persist, particularly in accurately representing all languages within tokenizer frameworks due to variations in language characters and other linguistic factors. Data cleaning [95], [35], [96], [49], [62], [65], [58], [68], [67], [89], [74], [75], [76], [77], [88], [94], [100], [103], [104], [107], [80], [106], [81], [108], [109], [82], [53], [110], [111], [83], [92], [113], [114], [115], [116], [117] Stemming [51], [67], [104], [108],…”

“…Transformer-based contextual embeddings, although not always explicitly mentioned in the utilized studies, are prevalent sentiment analysis tasks, indicating a growing trend towards leveraging transformer models for feature extraction in low-resource languages. However, several studies like [47], [48], [51], [94], [88], and [106] do not report specific feature extraction techniques, possibly due to the adoption of transformer architectures. Overall, the analysis underscores the diversity of feature extraction methods employed in sentiment analysis for low-resource languages, with a notable shift towards transformer-based approaches.…”

Applying Sentiment Product Reviews and Visualization for BI Systems in Vietnamese E-Commerce Website: Focusing on Vietnamese Context

“…Because the author only trained the data set in the hotel services context, this data training set can only be effective in this case study and is still not used popularly in other contexts. Vietnamese sentiment classification on one public dataset was studied in research [26]. The authors built five ensemble schemes and proved feature importance methods with five techniques.…”