Analysis of the Evolution of Advanced Transformer-Based Language Models: Experiments on Opinion Mining

TL;DR

This paper investigates how advanced transformer-based language models perform in opinion mining, comparing their capabilities and providing insights for researchers and practitioners to guide future developments.

Contribution

It offers a high-level comparison of state-of-the-art transformer models in opinion mining, highlighting their unique features and practical implications.

Findings

Transformer models outperform traditional RNNs in long text processing.

Key differences identified among leading transformer models.

Guidelines provided for future research and application in opinion mining.

Abstract

Opinion mining, also known as sentiment analysis, is a subfield of natural language processing (NLP) that focuses on identifying and extracting subjective information in textual material. This can include determining the overall sentiment of a piece of text (e.g., positive or negative), as well as identifying specific emotions or opinions expressed in the text, that involves the use of advanced machine and deep learning techniques. Recently, transformer-based language models make this task of human emotion analysis intuitive, thanks to the attention mechanism and parallel computation. These advantages make such models very powerful on linguistic tasks, unlike recurrent neural networks that spend a lot of time on sequential processing, making them prone to fail when it comes to processing long text. The scope of our paper aims to study the behaviour of the cutting-edge Transformer-based language models on opinion mining and provide a high-level comparison between them to highlight their key particularities. Additionally, our comparative study shows leads and paves the way for production engineers regarding the approach to focus on and is useful for researchers as it provides guidelines for future research subjects.