## 2 Literature Review
### Evolution of Target and Aspect-Based Sentiment Analysis  
The primary focus of conventional sentiment analysis was the assessment of the general sentiment at the sentence or document level. Nevertheless, this method was insufficient in capturing subtle opinions regarding particular elements or entities. In order to overcome this constraint, researchers have developed target-based and aspect-based sentiment analysis techniques. These approaches seek to offer a more detailed comprehension of sentiment specifically connected to particular aspects or entities [14]. Target-level sentiment analysis (TLSA) is a method used to determine the general sentiment linked to a specific entity, which is known as the target. Conversely, aspect-based sentiment analysis (ABSA) concentrates on several facets of an item, such as its quality, robustness, or pricing [39].  
The techniques used in target-level and aspect-based sentiment analysis vary from conventional machine learning methods to advanced deep learning and transformer-based models, which have greatly improved accuracy and contextual comprehension. Refs. [40; 41; 42] employed conventional methods that involved the use of algorithms such as Naive Bayes, Logistic Regression, and Support Vector Machines. These algorithms were used for extracting features and predicting sentiment in relation to specified entities or aspects.  
The efficacy of TLSA and ABSA has been further enhanced by the emergence of deep learning. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), which include Long Short-Term Memory (LSTM) networks and Gated Recurrent Units, have been instrumental in capturing intricate patterns and contextual information. The use of CNNs and LSTMs enhanced by attention mechanisms to concentrate more accurately on relevant text segments when assessing sentiment for specific aspects is highlighted in studies by [43; 44].  
Transformer-based models, specifically BERT, have significantly transformed the fields of TLSA and ABSA by offering an unparalleled level of comprehension in terms of context and semantics [45; 46]. These models achieve superior sentiment assessment performance by utilizing extensive pre-training on large datasets, which is followed by task-specific fine-tuning.  
Although BERT-based models are effective, they encounter problems such as the need for pre-training that is appropriate to a particular domain and the management of ambiguous contexts [47]. In order to address these constraints, large language models (LLMs), including LLaMa, ChatGPT, and Gemini, have been implemented for TLSA and ABSA tasks [7]. [PERSON]. [48] conducted experiments with the SemEval-2016 for the Restaurant and Laptop domains to investigate GPT's capabilities for the Aspect Sentiment Triplet Extraction (ASTE) subtask. In a similar manner, [PERSON]. [49] employed GPT to forecast sentiment scores for different elements derived from bakery reviews. Nevertheless, there is a scarcity of thorough comparison assessments on different LLMs in specific fields like finance.  
Building on this literature review, it is clear that preliminary investigations into the application of LLMs for ABSA and TLSA demonstrate potential; however, the specificity and complexity of domains such as finance necessitate a more comprehensive examination. Our research represents a significant effort to assess the efficacy of LLMs for TLSA in the financial sector. By conducting a comparative analysis of different LLMs, specifically ChatGPT versions 3.5, 4, and 4o, as well as the Gemini series--Gemini 1 Pro, Gemini 1.5 Flash, and Gemini 1.5 Pro--in conjunction with traditional methods like VADER, DistilFinRoBERTa, and DeBERTa-v3-base-ABSA-v1.1, our study assess their capabilities in the complex context of financial sentiment analysis.