Deep Content Understanding Toward Entity and Aspect Target Sentiment Analysis on Foundation Models

TL;DR

This paper introduces a new entity-aspect sentiment triplet extraction task, EASTE, and evaluates various transformer-based models and adaptation techniques, achieving state-of-the-art results in complex sentiment analysis.

Contribution

It proposes the EASTE task, extending ABSA by separating entities and aspects, and systematically evaluates multiple transformer models and fine-tuning methods for this task.

Findings

Transformer models can effectively perform EASTE with appropriate fine-tuning.

Model size and adaptation techniques significantly impact performance.

State-of-the-art results achieved on the SamEval-2016 dataset.

Abstract

Introducing Entity-Aspect Sentiment Triplet Extraction (EASTE), a novel Aspect-Based Sentiment Analysis (ABSA) task which extends Target-Aspect-Sentiment Detection (TASD) by separating aspect categories (e.g., food#quality) into pre-defined entities (e.g., meal, drink) and aspects (e.g., taste, freshness) which add a fine-gainer level of complexity, yet help exposing true sentiment of chained aspect to its entity. We explore the task of EASTE solving capabilities of language models based on transformers architecture from our proposed unified-loss approach via token classification task using BERT architecture to text generative models such as Flan-T5, Flan-Ul2 to Llama2, Llama3 and Mixtral employing different alignment techniques such as zero/few-shot learning, Parameter Efficient Fine Tuning (PEFT) such as Low-Rank Adaptation (LoRA). The model performances are evaluated on the SamEval-2016 benchmark dataset representing the fair comparison to existing works. Our research not only aims to achieve high performance on the EASTE task but also investigates the impact of model size, type, and adaptation techniques on task performance. Ultimately, we provide detailed insights and achieving state-of-the-art results in complex sentiment analysis.