An Efficient Model for Sentiment Analysis of Electronic Product Reviews in Vietnamese

TL;DR

This paper presents a fast and accurate sentiment analysis model for Vietnamese electronic product reviews using Self-attention neural networks, aiding businesses in understanding customer opinions efficiently.

Contribution

The paper introduces a novel application of Self-attention neural networks for Vietnamese sentiment analysis with high accuracy and rapid inference.

Findings

Achieved 90.16% accuracy in sentiment classification.

Model inference time is approximately 0.0124 seconds.

Demonstrated effectiveness for Vietnamese e-commerce reviews.

Abstract

In the past few years, the growth of e-commerce and digital marketing in Vietnam has generated a huge volume of opinionated data. Analyzing those data would provide enterprises with insight for better business decisions. In this work, as part of the Advosights project, we study sentiment analysis of product reviews in Vietnamese. The final solution is based on Self-attention neural networks, a flexible architecture for text classification task with about 90.16% of accuracy in 0.0124 second, a very fast inference time.