## 2 Related Works
### Deep Learning-Based Approaches
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Embedding** & **Tasks** & **Method** & **Metric** & **Dataset** \\ \hline
[78] & Various AI Techniques & Multiple AI Models & Review & Qualitative Analysis & \(\mathrm{N/A}\) \\ \hline
[79] & LLMs, Transformer & GPT-4, ChatGPT & NLP Tasks & Accuracy, F1 Score & Open-source datasets \\ \hline
[80] & Medical Data Analysis & Various ML Models & Comparative Study & Sensitivity, Specificity & Medical datasets \\ \hline
[81] & LLMs, Transfer Learning & GPT-3, BERT & Transfer Learning & Accuracy, Precision, Recall & Financial datasets \\ \hline
[82] & LoRA Fine-tuning & Llama-2 & Fine-tuning & Accuracy, F1 Score & Custom datasets \\ \hline
[83] & LLMs, Sentiment & GPT-4, BERT & Sentiment Analysis & Accuracy, Precision, Recall & Social media datasets \\ \hline
[84] & ChatGPT, Sentiment & \multirow{2}{*}{ChatGPT} & \multirow{2}{*}{Sentiment Analysis} & \multirow{2}{*}{Accuracy, F1 Score} & \multirow{2}{*}{Customer reviews} \\  & Analysis & & & & \\ \hline
[8] & LLMs, Reinforcement & \multirow{2}{*}{GPT-3, GPT-4} & \multirow{2}{*}{RLHF} & \multirow{2}{*}{Accuracy, F1 Score} & \multirow{2}{*}{
\begin{tabular}{c} Various NLP \\ benchmarks \\ \end{tabular} } \\  & Learning & & & & \\ \hline
[85] & LLMs, Multimodal & GPT-4, Multimodal & \multirow{2}{*}{Multimodal Tasks} & \multirow{2}{*}{Accuracy, F1 Score} & \multirow{2}{*}{Multimodal datasets} \\  & Learning & Models & & & \\ \hline
[86] & LLMs, Text Generation & GPT-3, GPT-4 & Text Generation & BLEU, ROUGE & Text corpora \\ \hline
[68] & Retrieval-Augmented & \multirow{2}{*}{GPT-4, LLMa} & Retrieval-Augmented & \multirow{2}{*}{Accuracy, F1 Score} & \multirow{2}{*}{
\begin{tabular}{c} Financial news \\ datasets \\ \end{tabular} } \\  & LLMs, Ethics & GPT-3, GPT-4 & Ethical Analysis & Qualitative Analysis & \(\mathrm{N/A}\) \\ \hline
[87] & LLMs, Robustness & GPT-3, GPT-4 & Adversarial Testing & Robustness Metrics & Adversarial datasets \\ \hline
[88] & LLMs, Translation & \multirow{2}{*}{
\begin{tabular}{c} GPT-4, Translation \\ Models \\ \end{tabular} } & \multirow{2}{*}{Machine Translation} & \multirow{2}{*}{BLEU, METEOR} & \multirow{2}{*}{Multilingual datasets} \\ \cline{1-1} \cline{5-5}
[89] & LLMs, Evaluation & \multirow{2}{*}{\
\begin{tabular}{c} GPT-3, GPT-4 \\ Methods \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} Comprehensive \\ Survey \\ \end{tabular} } & \multirow{2}{*}{Various Metrics} & \multirow{2}{*}{Various datasets \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of related studies based on common characteristics. In this study, we build on ARLMs, PTLMs, and RAG to establish a new framework called LFEAR. This framework promises to not merely detect but also to classify the meanings of various sentences found in textual forms of social media and in the financial comments made about products and services of financial institutions (e.g., Hellopeter). The LFEAR model retrieves real-time financial information on a dynamic basis by integrating RAG. This satisfies the need for a constant adaptation to not only emerging trends but also to the types of new language that these trends provoke [95]. LFEAR not only makes a more computationally efficient model but also heightens the intensity and granularity of the sentiment classifications. This allows for a much clearer representation of the actual sentiments expressed in the data. It is necessary, especially in the finance industry, to have a precise model that can influence decision-making based on the surrounding environment and the interaction of the customers with the product and service offer [96]. LFEAR leverages a continuous learning framework to incorporate financial information in a structured manner and seeks to build a model that is adaptive, accurate, and precise. LFEAR's goal is to obliterate the limits we have identified in the field of sentiment analysis and offer a model that is comprehensive, adaptive, and accurate enough to serve as a sentiment analysis engine in the financial domain [97].