## 3 Methodology
### Target-Level Sentiment Classification Using Traditional Methods  
This study utilizes three conventional models for financial sentiment analysis: VADER, DistilFinRoBERTa, and Deberta-v3-base-absa-v1.1. The VADER ([[https://github.com/cjhutto/vaderSentiment--accessed](https://github.com/cjhutto/vaderSentiment--accessed)]([https://github.com/cjhutto/vaderSentiment--accessed](https://github.com/cjhutto/vaderSentiment--accessed)) on 23 December 2024) tool, developed by [54], has been widely used by researchers in the field of financial sentiment analysis. It is a rule-based tool for performing document/sentence sentiment analysis. While specifically attuned to social media, VADER also performs well in other domains. Some notable studies that have utilized VADER in the financial domain include [24; 55; 56; 57]. DistilFinRoBERTa ([[https://huggingface.co/mrmn8488/distilroberta-finetuned-financial-news-sentiment-analysis--accessed](https://huggingface.co/mrmn8488/distilroberta-finetuned-financial-news-sentiment-analysis--accessed)]([https://huggingface.co/mrmn8488/distilroberta-finetuned-financial-news-sentiment-analysis--accessed](https://huggingface.co/mrmn8488/distilroberta-finetuned-financial-news-sentiment-analysis--accessed)) on 23 December 2024) is a lightweight variant of FinBERT, which is a model that was specifically pre-trained for understanding financial texts. This entails utilizing a financial collection of texts and refining the model for categorizing financial sentiment, incorporating resources like the in-house documents and annotations from John-Snow LAB and the Financial PhraseBank by [29]. DistilFinRoBERTa has been implemented in the financial sector for document/sentence sentiment analysis by scholars such as [58; 59]. DeBERTa-v3-base-ABSA-v1.1 ([[https://huggingface.co/yangheng/deberta-v3-base-absa-v1.1--accessed](https://huggingface.co/yangheng/deberta-v3-base-absa-v1.1--accessed)]([https://huggingface.co/yangheng/deberta-v3-base-absa-v1.1--accessed](https://huggingface.co/yangheng/deberta-v3-base-absa-v1.1--accessed)) on 23 December 2024) is a model specifically developed for ABSA. It uses the FAST-LCF-BERT model, which includes Microsoft/DeBERTa-v3-base and is sourced from PyABSA [60; 61]. This model is trained using a large dataset of over 30,000 ABSA samples. It is then fine-tuned with an additional 180,000 examples from various ABSA datasets ([[https://github.com/yangheng95/ABSADATastest--accessed](https://github.com/yangheng95/ABSADATastest--accessed)]([https://github.com/yangheng95/ABSADATastest--accessed](https://github.com/yangheng95/ABSADATastest--accessed)) on 23 December 2024), including SemEval-2014 Task 4 (Laptop14 and Restaurant14) [62], MAMS [63], SemEval-2016 Task 5 (Restaurant-16) [64], and other datasets specific to product categories such as T-shirts [65; 66] and televisions [67]. This model has been utilized by various researchers for ABSA tasks, such as [68; 69; 70]. We employ DeBERTa-v3-base-ABSA-v1.1 in two configurations: the pre-trained model as provided and a fine-tuned version developed through 5-fold cross-validation (5-fold cross-validation tests the model on the entire dataset by ensuring every data point is used for both training and testing but never at the same time. This helps avoid overfitting and provides a more reliable measure of model performance).