Vietnamese Sentiment Analysis Research Articles

Vietnamese Sentiment Analysis: An Overview and Comparative Study of Fine-tuning Pretrained Language Models

Sentiment Analysis (SA) is one of the most active research areas in the Natural Language Processing (NLP) field due to its potential for business and society. With the development of language representation models, numerous methods have shown promising efficiency in fine-tuning pre-trained language models in NLP downstream tasks. For Vietnamese, many available pre-trained language models were also released, including the monolingual and multilingual language models. Unfortunately, all of these models were trained on different architectures, pre-trained data, and pre-processing steps; consequently, fine-tuning these models can be expected to yield different effectiveness. In addition, there is no study focusing on evaluating the performance of these models on the same datasets for the SA task up to now. This article presents a fine-tuning approach to investigate the performance of different pre-trained language models for the Vietnamese SA task. The experimental results show the superior performance of the monolingual PhoBERT model and ViT5 model in comparison with previous studies and provide new state-of-the-art performances on five benchmark Vietnamese SA datasets. To the best of our knowledge, our study is the first attempt to investigate the performance of fine-tuning Transformer-based models on five datasets with different domains and sizes for the Vietnamese SA task.

Vietnamese Sentiment Analysis under Limited Training Data Based on Deep Neural Networks

The annotated dataset is an essential requirement to develop an artificial intelligence (AI) system effectively and expect the generalization of the predictive models and to avoid overfitting. Lack of the training data is a big barrier so that AI systems can broaden in several domains which have no or missing training data. Building these datasets is a tedious and expensive task and depends on the domains and languages. This is especially a big challenge for low‐resource languages. In this paper, we experiment and evaluate many various approaches on sentiment analysis problems so that they can still obtain high performances under limited training data. This paper uses the preprocessing techniques to clean and normalize the data and generate the new samples from the limited training dataset based on many text augmentation techniques such as lexicon substitution, sentence shuffling, back translation, syntax‐tree transformation, and embedding mixup. Several experiments have been performed for both well‐known machine learning‐based classifiers and deep learning models. We compare, analyze, and evaluate the results to indicate the advantage and disadvantage points of the techniques for each approach. The experimental results show that the data augmentation techniques enhance the accuracy of the predictive models; this promises that smart systems can be applied widely in several domains under limited training data.

A review: preprocessing techniques and data augmentation for sentiment analysis

In literature, the machine learning-based studies of sentiment analysis are usually supervised learning which must have pre-labeled datasets to be large enough in certain domains. Obviously, this task is tedious, expensive and time-consuming to build, and hard to handle unseen data. This paper has approached semi-supervised learning for Vietnamese sentiment analysis which has limited datasets. We have summarized many preprocessing techniques which were performed to clean and normalize data, negation handling, intensification handling to improve the performances. Moreover, data augmentation techniques, which generate new data from the original data to enrich training data without user intervention, have also been presented. In experiments, we have performed various aspects and obtained competitive results which may motivate the next propositions.

VLSP SHARED TASK: SENTIMENT ANALYSIS

Sentiment analysis is a natural language processing (NLP) task of identifying orextracting the sentiment content of a text unit. This task has become an active research topic since the early 2000s. During the two last editions of the VLSP workshop series, the shared task on Sentiment Analysis (SA) for Vietnamese has been organized in order to provide an objective evaluation measurement about the performance (quality) of sentiment analysis tools, and encouragethe development of Vietnamese sentiment analysis systems, as well as to provide benchmark datasets for this task. The rst campaign in 2016 only focused on the sentiment polarity classication, with a dataset containing reviews of electronic products. The second campaign in 2018 addressed the problem of Aspect Based Sentiment Analysis (ABSA) for Vietnamese, by providing two datasets containing reviews in restaurant and hotel domains. These data are accessible for research purpose via the VLSP website vlsp.org.vn/resources. This paper describes the built datasets as well as the evaluation results of the systems participating to these campaigns.

A Valence-Totaling Model for Vietnamese sentiment classification

Many researchers, applications and fields of study have researched and used many works concerning the sentiment classification. Each model (or method) of the sentiment analysis has many advantages and many disadvantages. Thus, we see that the opinion classification is an extremely important field of research. In this study, we have proposed a Valence-Totaling Model for Vietnamese (called VTMfV, a new model for Vietnamese sentiment classification) to classify many Vietnamese documents. First of all, we built a new Vietnamese sentiment dictionary which contains sentiment-bearing Vietnamese words such as negative Vietnamese words, positive Vietnamese words and neutral Vietnamese words. The Jaccard Measure (JM) is a similarity measure between two words (or two vectors); our Vietnamese sentiment dictionary has been created using JM. We call the Vietnamese sentiment dictionary “VSD_JM”. JM has been used in many researches of the English sentiment classification; however, it has not yet been used in any study of the Vietnamese sentient classification. From this moment, JM can be applied for the researches of the Vietnamese sentiment analysis. Then, our VTMfV has used our VSD_JM to classify the Vietnamese documents. We have processed all kinds of Vietnamese sentences. Finally, we have used the VTMfV to classify 30,000 Vietnamese documents which include the 15,000 positive Vietnamese documents and the 15,000 negative Vietnamese documents. We have achieved accuracy in 63.9% of our Vietnamese testing data set. VTMfV is not dependent on the special domain. VTMfV is also not dependent on the training data set and there is no training stage in this VTMfV. From our results in this work, our VTMfV can be applied in the different fields of the Vietnamese natural language processing. In addition, our TCMfV can be applied to many other languages such as Spanish, Korean, etc. It can also be applied to the big data set sentiment classification in Vietnamese and can classify millions of the Vietnamese documents.