BERT-Based Model and LLMs-Generated Synthetic Data for Conflict Sentiment Identification in Aspect-Based Sentiment Analysis
Authors: Nuryani Nuryani , Munir Rinaldi , Purwarianti Ayu , Puji Lestari Dessi 
Published: February 2025

Aim/Purpose: Most research on aspect-based sentiment analysis (ABSA) classifies sentiment polarity into two classes (positive and negative) or three classes (positive, negative, and neutral) and does not include conflict sentiment. This study investigates the four-class sentiment classification (positive, negative, neutral, and conflict) and proposes a BERT-based model for identifying conflicting sentiment in ABSA. Furthermore, we employ the open-source large language models (LLMs) created by Meta, Llama 3, for generating synthetic data to support research on four-class sentiment classification in ABSA.

Background: Public opinions and experiences on product reviews, social events, political movements, etc. can be used for exploring customer behavior, predicting customer preferences, understanding public sentiment, etc., so it becomes an important component in the decision-making process. Providing an accurate opinion will enable an individual, business, or organization to have an in-formed judgement before making a decision.

Methodology: This study utilizes a methodology that includes generating synthetic data to augment the original datasets, designing the input representation, detecting aspect categories, performing a multi-label sentiment classification, and rep-resenting sentiment in a four-class sentiment classification.

Contribution: This study provides an investigation on the four-class sentiment classification (positive, negative, neutral, and conflict) and proposes a BERT-based method to identify aspects with conflicting sentiment in ABSA. Moreover, it also evaluates Llama 3 for generating synthetic data to address the issues related to the data scarcity and imbalance datasets in the research of four-class senti-ment classification in ABSA.

Findings: The investigation of the four-class sentiment classification task in ABSA demonstrates that identifying conflict sentiment is challenging for several reasons. Among them are (1) the lack of a public dataset for this research; (2) the small amount of data with conflict labels in the available dataset resulting in an imbalanced dataset; (3) conflict sentiment is a complex sentiment con-taining both positive and negative sentiments; and (4) conflicting sentiments are usually expressed in long and complicated sentences and involve implicit aspects. Our solution to these challenges involved generating synthetic data using Llama 3 and designing a BERT-based model on multi-label aspects for identifying aspect with conflict sentiment.

Recommendations for Practitioners: Most existing ABSA models with four-class sentiment classification are con-ducted for the product reviews (mostly in the restaurant domain) and in high-resource languages (mainly in English). Therefore, users may need to make some adjustments to different domains and languages.

Recommendation for Researchers: Due to the limited datasets available for research in aspect-based sentiment analysis with four-class sentiment classification, the development of a dataset to support this research is urgently needed.

Impact on Society: By providing more accurate sentiment through aspect-based sentiment analy-sis, this study can better help people, organizations, or companies in get a view or an opinion about any product, service, or candidate in an electoral vote.

Future Research: Future research on aspect-based sentiment analysis could utilize the large language models (LLMs) for conducting ABSA tasks including aspect term extraction, aspect category detection, and its sentiment polarities. We could also focus on evaluating the model for cross-domain and cross-language AB-SA system.